----------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/uctqa20/Dropbox/Land&UrbanizationProject/Analysis/Replication 
> Material/replication_jop.log
  log type:  text
 opened on:  16 Dec 2021, 13:00:25

. 
. /*
> *** code to generate variables ***
> 
> *these variables are already generated and contained in the dataset, but if scho
> lars
> *what to check our coding against the raw variables, our code for generating the
>  cleaned
> *variables is here:
> 
> gen trust_pay_back = 1 if lexp_q11 == 2
> replace trust_pay_back = 0 if lexp_q11 == 1
> replace trust_pay_back = 0 if lexp_q11 == 3
> 
> gen help_collect_donations = 1 if lexp_q14 == 1
> replace help_collect_donations = 1 if lexp_q14 == 2
> replace help_collect_donations = 0 if lexp_q14 == 3
> replace help_collect_donations = 0 if lexp_q14 == 4
> 
> gen exp_title = 0 if lexp_q10 == 3
> replace exp_title = 0 if lexp_q10 == 4
> replace exp_title = 1 if lexp_q10 == 1
> replace exp_title = 1 if lexp_q10 == 2
> 
> gen exp_highinc = 1 if lexp_q9 == 1
> replace exp_highinc = 0 if lexp_q9 != 1 & lexp_q9 != .
> 
> gen exp_lowinc = 1 if lexp_q9 == 3
> replace exp_lowinc = 0 if lexp_q9 != 3 & lexp_q9 != .
> 
> gen exp_migrant = 1 if lexp_q8 == 2
> replace exp_migrant = 0 if lexp_q8 == 1
> 
> gen exp_age50 = 1 if lexp_q7 == 2
> replace exp_age50 = 0 if lexp_q7 == 1
> 
> gen exp_male = 1 if lexp_q6 == 1
> replace exp_male = 0 if lexp_q6 == 2
> 
> *to code exp_co_ethnic, we can simply use lexp_q5 as is done below, but we first
> *code each respondent's ethnicity to ensure lexp_q5 accurately captures whether 
> or not
> *the hypothetical neighbor is a co-ethnic or not. 
> 
> *Malawi ethnicity
> tab demo_q4 if mal_border == 1
> 
> gen resp_chewa = 1 if demo_q4 == 4
> replace resp_chewa = 0 if demo_q4 != 4 & demo_q4 != .
> 
> gen resp_lambya = 1 if demo_q4 == 18
> replace resp_lambya = 0 if demo_q4 != 18 & demo_q4 != .
> 
> gen resp_lomwe = 1 if demo_q4 == 20
> replace resp_lomwe = 0 if demo_q4 != 20 & demo_q4 != .
> 
> gen resp_manganja = 1 if demo_q4 == 28
> replace resp_manganja = 0 if demo_q4 != 28 & demo_q4 != .
> 
> gen resp_ndali = 1 if demo_q4 == 34
> replace resp_ndali = 0 if demo_q4 != 34 & demo_q4 != .
> 
> gen resp_ngoni = 1 if demo_q4 == 35
> replace resp_ngoni = 0 if demo_q4 != 35 & demo_q4 != .
> 
> gen resp_nkhonde = 1 if demo_q4 == 37
> replace resp_nkhonde = 0 if demo_q4 != 37 & demo_q4 != .
> 
> gen resp_sena = 1 if demo_q4 == 43
> replace resp_sena = 0 if demo_q4 != 43 & demo_q4 != .
> 
> gen resp_senga = 1 if demo_q4 == 44
> replace resp_senga = 0 if demo_q4 != 44 & demo_q4 != .
> 
> gen resp_sukwa = 1 if demo_q4 == 47
> replace resp_sukwa = 0 if demo_q4 != 47 & demo_q4 != .
> 
> gen resp_tonga = 1 if demo_q4 == 51
> replace resp_tonga = 0 if demo_q4 != 51 & demo_q4 != .
> 
> gen resp_tumbuka = 1 if demo_q4 == 52
> replace resp_tumbuka = 0 if demo_q4 != 52 & demo_q4 != .
> 
> gen resp_yao = 1 if demo_q4 == 55
> replace resp_yao = 0 if demo_q4 != 55 & demo_q4 != .
> 
> *gen resp_no_eth = 1 if demo_q4 == "Don't Know/Refuse to answer" | demo_q4 == "D
> oes not identify in those terms (Malawian only)"
> *replace resp_no_eth = 0 if demo_q4 != "Don't Know/Refuse to answer" & demo_q4 !
> = "Does not identify in those terms (Malawian only)" & q_103 != ""
> 
> 
> *Zambia ethnicity
> tab demo_q4 if zam_border == 1
> 
> gen resp_bemba = 1 if demo_q4 == 1
> replace resp_bemba = 0 if demo_q4 != 1 & demo_q4 != .
> 
> gen resp_bisa = 1 if demo_q4 == 2
> replace resp_bisa = 0 if demo_q4 != 2 & demo_q4 != .
> 
> gen resp_bwile = 1 if demo_q4 == 3
> replace resp_bwile = 0 if demo_q4 != 3 & demo_q4 != .
> 
> gen resp_english = 1 if demo_q4 == 7
> replace resp_english = 0 if demo_q4 != 7 & demo_q4 != .
> 
> gen resp_ila = 1 if demo_q4 == 9
> replace resp_ila = 0 if demo_q4 != 9 & demo_q4 != .
> 
> gen resp_kaonde = 1 if demo_q4 == 12
> replace resp_kaonde = 0 if demo_q4 != 12 & demo_q4 != .
> 
> gen resp_kunda = 1 if demo_q4 == 15
> replace resp_kunda = 0 if demo_q4 != 15 & demo_q4 != .
> 
> gen resp_lala = 1 if demo_q4 == 16
> replace resp_lala = 0 if demo_q4 != 16 & demo_q4 != .
> 
> gen resp_lamba = 1 if demo_q4 == 17
> replace resp_lamba = 0 if demo_q4 != 17 & demo_q4 != .
> 
> gen resp_lenje = 1 if demo_q4 == 19
> replace resp_lenje = 0 if demo_q4 != 19 & demo_q4 != .
> 
> gen resp_lozi = 1 if demo_q4 == 21
> replace resp_lozi = 0 if demo_q4 != 21 & demo_q4 != .
> 
> gen resp_lunda = 1 if demo_q4 == 23
> replace resp_lunda = 0 if demo_q4 != 23 & demo_q4 != .
> 
> gen resp_lungu = 1 if demo_q4 == 24
> replace resp_lungu = 0 if demo_q4 != 24 & demo_q4 != .
> 
> gen resp_luvale = 1 if demo_q4 == 26
> replace resp_luvale = 0 if demo_q4 != 26 & demo_q4 != .
> 
> gen resp_mambwe = 1 if demo_q4 == 27
> replace resp_mambwe = 0 if demo_q4 != 27 & demo_q4 != .
> 
> gen resp_namwanga = 1 if demo_q4 == 33
> replace resp_namwanga = 0 if demo_q4 != 33 & demo_q4 != .
> 
> gen resp_nsenga = 1 if demo_q4 == 39
> replace resp_nsenga = 0 if demo_q4 != 39 & demo_q4 != .
> 
> gen resp_nyanja = 1 if demo_q4 == 40
> replace resp_nyanja = 0 if demo_q4 != 40 & demo_q4 != .
> 
> gen resp_nyika = 1 if demo_q4 == 41
> replace resp_nyika = 0 if demo_q4 != 41 & demo_q4 != .
> 
> gen resp_soli = 1 if demo_q4 == 45
> replace resp_soli = 0 if demo_q4 != 45 & demo_q4 != .
> 
> gen resp_tabwa = 1 if demo_q4 == 48
> replace resp_tabwa = 0 if demo_q4 != 48 & demo_q4 != .
> 
> *gen resp_tonga = 1 if demo_q4 == 51
> *replace resp_tonga = 0 if demo_q4 != 51 & demo_q4 != .
> 
> gen resp_ushi = 1 if demo_q4 == 54
> replace resp_ushi = 0 if demo_q4 != 54 & demo_q4 != .
> 
> 
> *recode "others" that fit in the above groupings and code all others as "OTHER"
> tab demo_q4_O if mal_border == 1
> tab demo_q4_O if zam_border == 1
> 
> replace resp_bemba = 1 if demo_q4_O == "Bemba"
> replace resp_ndali = 1 if demo_q4_O == "Chindali"
> replace resp_ndali = 1 if demo_q4_O == "M'ndali"
> replace resp_mambwe = 1 if demo_q4_O == "Mambwe"
> replace resp_mambwe = 1 if demo_q4_O == "Mmambwe"
> replace resp_nyika = 1 if demo_q4_O == "Mnyika"
> replace resp_namwanga = 1 if demo_q4_O == "Munamwanga"
> replace resp_nyika = 1 if demo_q4_O == "Munyika"
> replace resp_sukwa = 1 if demo_q4_O == "Musukwa"
> replace resp_namwanga = 1 if demo_q4_O == "Mwinamwanga"
> replace resp_namwanga = 1 if demo_q4_O == "Namwagwa"
> replace resp_namwanga = 1 if demo_q4_O == "Namwanga"
> replace resp_namwanga = 1 if demo_q4_O == "Namwanga (Zambian language)"
> replace resp_ndali = 1 if demo_q4_O == "Ndali"
> replace resp_ngoni = 1 if demo_q4_O == "Ngoni"
> replace resp_namwanga = 1 if demo_q4_O == "Nyamwanga"
> *replace resp_nyanja = 1 if demo_q4_O == "Nyanja"
> replace resp_nyika = 1 if demo_q4_O == "Nyika"
> replace resp_nyika = 1 if demo_q4_O == "Nyika/Nyiha"
> replace resp_sukwa = 1 if demo_q4_O == "Sukwa"
> replace resp_lambya = 1 if demo_q4_O == "lambya"
> replace resp_mambwe = 1 if demo_q4_O == "mabwe"
> replace resp_mambwe = 1 if demo_q4_O == "mambwe"
> replace resp_nyika = 1 if demo_q4_O == "mnyika"
> replace resp_namwanga = 1 if demo_q4_O == "mwinamwanga"
> replace resp_namwanga = 1 if demo_q4_O == "namwanga"
> replace resp_ndali = 1 if demo_q4_O == "ndali"
> replace resp_namwanga = 1 if demo_q4_O == "nyamwanga"
> replace resp_nyika = 1 if demo_q4_O == "nyika"
> replace resp_nyika = 1 if demo_q4_O == "Mnyiha"
> replace resp_nyika = 1 if demo_q4_O == "Munyiha"
> replace resp_nyika = 1 if demo_q4_O == "Nyiha"
> replace resp_nyika = 1 if demo_q4_O == "mnyiha"
> replace resp_nyika = 1 if demo_q4_O == "nyiha"
> 
> replace resp_lambya = 1 if demo_q4_O == "Lambia"
> replace resp_lambya = 1 if demo_q4_O == "Labiya"
> replace resp_lambya = 1 if demo_q4_O == "Lambiya"
> replace resp_lambya = 1 if demo_q4_O == "Lambya"
> replace resp_lambya = 1 if demo_q4_O == "Mulambya"
> replace resp_ndali = 1 if demo_q4_O == "Mundali"
> replace resp_ushi = 1 if demo_q4_O == "Mwaushi"
> replace resp_tabwa = 1 if demo_q4_O == "Tabwa"
> replace resp_tonga = 1 if demo_q4_O == "Tonga from Malawi."
> replace resp_kunda = 1 if demo_q4_O == "chikunda"
> replace resp_lambya = 1 if demo_q4_O == "labia"
> replace resp_lambya = 1 if demo_q4_O == "labya"
> replace resp_lambya = 1 if demo_q4_O == "lambai"
> replace resp_lambya = 1 if demo_q4_O == "lambia"
> replace resp_lambya = 1 if demo_q4_O == "lambiya"
> *replace resp_lambya = 1 if demo_q4_O == "lambya"
> replace resp_lungu = 1 if demo_q4_O == "mulungu"
> replace resp_nyika = 1 if demo_q4_O == "munyiha"
> replace resp_ngoni = 1 if demo_q4_O == "nguni"
> replace resp_tumbuka = 1 if demo_q4_O == "tubuka"
> 
> 
> gen resp_other = 1 if demo_q4 == 56 & resp_chewa == 0 & resp_lambya == 0 & resp_
> lomwe == 0 & resp_manganja == 0 & resp_ndali == 0 & resp_ngoni == 0 & resp_nkhon
> de == 0 & ///
> resp_sena == 0 & resp_senga == 0 & resp_sukwa == 0 & resp_tonga == 0 & resp_tumb
> uka == 0 & resp_yao == 0 & resp_bemba == 0 & resp_bisa == 0 & ///
> resp_bwile == 0 & resp_english == 0 & resp_ila == 0 & resp_kaonde == 0 & resp_ku
> nda == 0 & resp_lala == 0 & resp_lamba == 0 & resp_lenje == 0 & ///
> resp_lozi == 0 & resp_lunda == 0 & resp_lungu == 0 & resp_luvale == 0 & resp_mam
> bwe == 0 & resp_namwanga == 0 & resp_nsenga == 0 & ///
> resp_nyika == 0 & resp_soli == 0 & resp_tabwa == 0 & resp_ushi == 0
> 
> replace resp_other = 0 if demo_q4 != 56 & demo_q4 != . & (resp_chewa == 1 | resp
> _lambya == 1 | resp_lomwe == 1 | resp_manganja == 1 | resp_ndali == 1 | resp_ngo
> ni == 1 | resp_nkhonde == 1 | ///
> resp_sena == 1 | resp_senga == 1 | resp_sukwa == 1 | resp_tonga == 1 | resp_tumb
> uka == 1 | resp_yao == 1 | resp_bemba == 1 | resp_bisa == 1 | ///
> resp_bwile == 1 | resp_english == 1 | resp_ila == 1 | resp_kaonde == 1 | resp_ku
> nda == 1 | resp_lala == 1 | resp_lamba == 1 | resp_lenje == 1 | ///
> resp_lozi == 1 | resp_lunda == 1 | resp_lungu == 1 | resp_luvale == 1 | resp_mam
> bwe == 1 | resp_namwanga == 1 | resp_nsenga == 1 | ///
> resp_nyika == 1 | resp_soli == 1 | resp_tabwa == 1 | resp_ushi == 1)
> 
> replace resp_other = 0 if resp_other == . & resp_chewa != .
> 
> 
> *check to see if we think the person got non-co-ethnic but really got co-ethnic
> 
> gen exp_co_ethnic = 1 if lexp_q5 == 1
> replace exp_co_ethnic = 0 if lexp_q5 == 2
> 
> replace exp_co_ethnic = 1 if exp_chewa == 1 & resp_chewa == 1
> replace exp_co_ethnic = 1 if exp_tumbuka == 1 & resp_tumbuka == 1
> replace exp_co_ethnic = 1 if exp_ngoni == 1 & resp_ngoni == 1
> replace exp_co_ethnic = 1 if exp_lambya == 1 & resp_lambya == 1
> replace exp_co_ethnic = 1 if exp_yao == 1 & resp_yao == 1
> replace exp_co_ethnic = 1 if exp_lomwe == 1 & resp_lomwe == 1
> replace exp_co_ethnic = 1 if exp_senga == 1 & resp_senga == 1
> replace exp_co_ethnic = 1 if exp_ndali == 1 & resp_ndali == 1
> replace exp_co_ethnic = 1 if exp_namwanga == 1 & resp_namwanga == 1
> replace exp_co_ethnic = 1 if exp_bemba == 1 & resp_bemba == 1
> replace exp_co_ethnic = 1 if exp_bisa == 1 & resp_bisa == 1
> replace exp_co_ethnic = 1 if exp_nyika == 1 & resp_nyika == 1
> *replace exp_co_ethnic = 1 if exp_nyanja == 1 & resp_nyanja == 1
> 
> 
> *those with title from the government or some other landlord (rather than via ch
> ief etc.) 
> gen have_gov_title = 1 if land_q9 == 2 & (land_q10 == 1 | land_q10 == 2 | land_q
> 10 == 6)
> replace have_gov_title = 0 if land_q9 == 1 /* those without land title */
> replace have_gov_title = 0 if land_q9 == 3 /* those who do not own any land */
> replace have_gov_title = 0 if land_q9 == 2 & (land_q10 == 3 | land_q10 == 4 | la
> nd_q10 == 5 | land_q10 == 7) /* those with traditional land title */
> 
> 
> *exp_maj_eth
> *now indicator for whether or not the hypothetical neighbor is from the majority
>  or plurality
> gen exp_maj_eth = .
> 
> foreach i in chewa lambya lomwe ndali ngoni ///
> senga tumbuka yao namwanga bemba bisa nyika {
>         replace exp_maj_eth = 1 if exp_`i' == 1 & majority == "`i'"
>         replace exp_maj_eth = 0 if exp_`i' == 0 & majority == "`i'"
>         }
> *
> 
> *shared identities
> *exp_co_wealth exp_co_migrant exp_co_age exp_co_gender
> 
> gen exp_co_wealth = .
> 
> replace exp_co_wealth = 1 if demo_q25 == 1 & exp_highinc == 1
> replace exp_co_wealth = 1 if demo_q25 == 2  & exp_highinc == 1
> replace exp_co_wealth = 0 if demo_q25 == 1 & exp_highinc == 0
> replace exp_co_wealth = 0 if demo_q25 == 2  & exp_highinc == 0
> 
> replace exp_co_wealth = 1 if demo_q25 == 3 & exp_highinc == 0 & exp_lowinc == 0
> replace exp_co_wealth = 0 if demo_q25 == 3 & (exp_highinc == 1 | exp_lowinc == 1
> )
> 
> replace exp_co_wealth = 1 if demo_q25 == 4 & exp_lowinc == 1
> replace exp_co_wealth = 0 if demo_q25 == 4 & exp_lowinc == 0
> 
> gen exp_co_migrant = 1 if demo_q10 == 1 & exp_migrant == 1
> replace exp_co_migrant = 1 if demo_q10 != 1 & demo_q10 != 6 & demo_q10 != . & ex
> p_migrant == 0
> replace exp_co_migrant = 0 if demo_q10 == 1 & exp_migrant == 0
> replace exp_co_migrant = 0 if demo_q10 != 1 & demo_q10 != 6 & demo_q10 != . & ex
> p_migrant == 1
> 
> gen exp_co_age = 1 if exp_age50 == 1 & demo_q2 >=38
> replace exp_co_age = 0 if exp_age50 == 1 & demo_q2 <38
> replace exp_co_age = 1 if exp_age50 == 0 & demo_q2 <=37
> replace exp_co_age = 0 if exp_age50 == 0 & demo_q2 >37
> 
> gen exp_co_gender = 1 if exp_male == 1 & demo_q1 == 1
> replace exp_co_gender = 0 if exp_male == 1 & demo_q1 == 2
> replace exp_co_gender = 0 if exp_male == 0 & demo_q1 == 1
> replace exp_co_gender = 1 if exp_male == 0 & demo_q1 == 2
> 
> 
> *respondent characteristics
> resp_highinc resp_lowinc resp_migrant age resp_male
> 
> gen resp_highinc = .
> replace resp_highinc = 1 if demo_q25 == 1 
> replace resp_highinc = 1 if demo_q25 == 2
> replace resp_highinc = 0 if demo_q25 == 3
> replace resp_highinc = 0 if demo_q25 == 4
> 
> gen resp_lowinc = .
> replace resp_lowinc = 1 if demo_q25 == 4 
> replace resp_lowinc = 0 if demo_q25 == 3
> replace resp_lowinc = 0 if demo_q25 == 1 
> replace resp_lowinc = 0 if demo_q25 == 2
> 
> gen resp_migrant = 1 if demo_q10 == 1
> replace resp_migrant = 0 if demo_q10 != 1 & demo_q10 != 6 & demo_q10 != .
> 
> rename demo_q2 age
> 
> gen resp_male = 1 if demo_q1 == 1
> replace resp_male = 0 if demo_q1 == 2
> 
> 
> */
. 
. 
. lab var single_male "Male Head (Unmarried)"

. lab var single_female "Female Head (Unmarried)"

. lab var age "Age"

. lab var resp_migrant "New Migrant"

. lab var resp_migrant10 "Migrant (10 years)"

. lab var outsider "Feels Like an Outsider"

. lab var local "Considered Local" 

. lab var native "Born in Village"

. lab var resp_highinc "High"

. lab var resp_lowinc "Low"

. lab var cash_income "Earns a Cash Income"

. lab var primary_schooling "Primary"

. lab var secondary_schooling "Any Secondary"

. lab var post_secondary_schooling "Any Post-Secondary"

. lab var secondaryplus "Secondary or Post-Secondary"

. lab var not_obligated "Does Not Feel Obligation to Others"

. lab var aglanduse "Agricultural Land Use"

. lab var local_obligation_to_help "Local Obligation" 

. lab var not_contribute "No Contributions in the Past Year"

. lab var contributed "Contributed in the Past Year"

. lab var land_size "Size of Land (hectares)"

. lab var land_not_agriculture "Primary use of Land NOT Agriculture"

. lab var aglanduse "Agricultural Land"

. lab var Acquiredfamily "Inherited Land"

. lab var Purchased "Purchased Land"

. lab var resp_maj_eth "Majority Ethnic Group"

. 
. 
. ********** Article **********
. 
. 
. *** Figure 1 ***
. 
. *Figure 1a
. * countries pooled, restricted, individual cooperation outcome
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,242
                                                F(8, 767)         =      23.39
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0283
                                                Root MSE          =     .49153

                                  (Std. Err. adjusted for 768 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0422636   .0117693    -3.59   0.000    -.0653674   -.0191598
  exp_highinc |   .0149003   .0134586     1.11   0.269    -.0115198    .0413203
   exp_lowinc |  -.0547126    .015533    -3.52   0.000    -.0852049   -.0242204
  exp_migrant |  -.1011336   .0117836    -8.58   0.000    -.1242655   -.0780018
    exp_age50 |  -.0143643    .012015    -1.20   0.232    -.0379505    .0092219
     exp_male |  -.0216041   .0114617    -1.88   0.060    -.0441042     .000896
exp_co_ethnic |   .0531214   .0114015     4.66   0.000     .0307395    .0755033
   mal_border |  -.0968534   .0137065    -7.07   0.000      -.12376   -.0699467
        _cons |    .665635   .0174502    38.14   0.000     .6313791    .6998909
-------------------------------------------------------------------------------

. est sto Trust_main, title(Ind. Coop.)

. *the plot
. coefplot Trust_main, drop(_cons) xline(0) omitted baselevels msymbol(d)  ///
> levels(95)  xtitle({bf:Estimated Change in Perceived Individ. Coop.}, size(small
> )) legend(off) xscale(range(-.15 .05)) xlabel(-.15(.05).05) ///
> headings(exp_title = "{bf:Title}" exp_highinc = "{bf:Income}" exp_migrant = "{bf
> :Migration Status}" exp_age50 = "{bf:Age}" exp_male = "{bf:Gender}" exp_co_ethni
> c = "{bf:Ethnicity}" mal_border = "{bf:Sample}", labsize(2.5)) ///
> graphregion(color(white)) grid(between glcolor(white)) legend(label(1 "Success I
> ndex") row(1)  size(small)) ylab(, labs(2.5)) xlab(, labs(2.5))

. *graph export trust_base_pool.pdf, replace
. *graph save trust_base_pool.gph, replace
. 
. *Fgure 1b
. * countries pooled, restricted, community cooperation outcome
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,975
                                                F(8, 766)         =       7.97
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0094
                                                Root MSE          =     .43306

                                  (Std. Err. adjusted for 767 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0324375   .0102568    -3.16   0.002    -.0525722   -.0123029
  exp_highinc |  -.0139679   .0122484    -1.14   0.254    -.0380124    .0100766
   exp_lowinc |  -.0443234   .0124814    -3.55   0.000    -.0688252   -.0198215
  exp_migrant |  -.0470549   .0107545    -4.38   0.000    -.0681666   -.0259432
    exp_age50 |   -.015369   .0107509    -1.43   0.153    -.0364737    .0057358
     exp_male |  -.0120076   .0107347    -1.12   0.264    -.0330805    .0090652
exp_co_ethnic |     .02331   .0099701     2.34   0.020      .003738     .042882
   mal_border |  -.0409129   .0118696    -3.45   0.001    -.0642137   -.0176121
        _cons |    .828205   .0149902    55.25   0.000     .7987782    .8576319
-------------------------------------------------------------------------------

. est sto Help_main, title(Comm. Coop.)

. *the plot
. coefplot Help_main, drop(_cons) xline(0) omitted baselevels msymbol(d)  ///
> levels(95)  xtitle({bf:Estimated Change in Perceived Comm. Coop.}, size(small)) 
> legend(off) xscale(range(-.15 .05)) xlabel(-.15(.05).05) ///
> headings(exp_title = "{bf:Title}" exp_highinc = "{bf:Income}" exp_migrant = "{bf
> :Migration Status}" exp_age50 = "{bf:Age}" exp_male = "{bf:Gender}" exp_co_ethni
> c = "{bf:Ethnicity}" mal_border = "{bf:Sample}", labsize(2.5)) ///
> graphregion(color(white)) grid(between glcolor(white)) legend(label(1 "Success I
> ndex") row(1)  size(small)) ylab(, labs(2.5)) xlab(, labs(2.5))

. *graph export help_base_pool.pdf, replace
. *graph save help_base_pool.gph, replace
. 
. 
. 
. *** Table 1 ***
. 
. *Malawi Restricted (Customary Land Rights) (4)
. *individual
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic if mal_border == 1 & have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      3,629
                                                F(7, 351)         =      12.31
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0210
                                                Root MSE          =     .49524

                                  (Std. Err. adjusted for 352 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0427326   .0171256    -2.50   0.013    -.0764143   -.0090509
  exp_highinc |   .0168175   .0181457     0.93   0.355    -.0188705    .0525054
   exp_lowinc |  -.0357235   .0215272    -1.66   0.098     -.078062     .006615
  exp_migrant |  -.0943327   .0166392    -5.67   0.000    -.1270579   -.0616076
    exp_age50 |   -.028412   .0177472    -1.60   0.110    -.0633162    .0064922
     exp_male |  -.0332421   .0166428    -2.00   0.047    -.0659743   -.0005099
exp_co_ethnic |     .08253   .0158704     5.20   0.000     .0513169    .1137431
        _cons |   .5568018   .0241914    23.02   0.000     .5092235    .6043801
-------------------------------------------------------------------------------

. *community
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic if mal_border == 1 & have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      3,518
                                                F(7, 351)         =       3.04
                                                Prob > F          =     0.0041
                                                R-squared         =     0.0057
                                                Root MSE          =     .44468

                                  (Std. Err. adjusted for 352 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0364252   .0144547    -2.52   0.012    -.0648539   -.0079965
  exp_highinc |  -.0199611   .0181336    -1.10   0.272    -.0556252    .0157031
   exp_lowinc |  -.0176647   .0175297    -1.01   0.314    -.0521413    .0168118
  exp_migrant |  -.0374095   .0147659    -2.53   0.012    -.0664502   -.0083688
    exp_age50 |   -.024602   .0154388    -1.59   0.112    -.0549662    .0057622
     exp_male |  -.0110034   .0157837    -0.70   0.486     -.042046    .0200392
exp_co_ethnic |   .0284026   .0144291     1.97   0.050     .0000242    .0567811
        _cons |   .7793337   .0209833    37.14   0.000     .7380649    .8206026
-------------------------------------------------------------------------------

. 
. *Zambia Restricted (Customary Land Rights) (5)
. *individual
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic if zam_border == 1 & have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      3,613
                                                F(7, 415)         =       9.90
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0208
                                                Root MSE          =     .48745

                                  (Std. Err. adjusted for 416 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0422743   .0161778    -2.61   0.009     -.074075   -.0104735
  exp_highinc |   .0112796    .020049     0.56   0.574    -.0281305    .0506898
   exp_lowinc |  -.0744642   .0222517    -3.35   0.001    -.1182044   -.0307241
  exp_migrant |  -.1085957    .016593    -6.54   0.000    -.1412126   -.0759789
    exp_age50 |  -.0003184   .0161446    -0.02   0.984    -.0320538     .031417
     exp_male |  -.0105828   .0157532    -0.67   0.502    -.0415488    .0203831
exp_co_ethnic |   .0239004   .0160626     1.49   0.138    -.0076737    .0554745
        _cons |   .6796638   .0216294    31.42   0.000     .6371471    .7221806
-------------------------------------------------------------------------------

. *community
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic if zam_border == 1 & have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      3,457
                                                F(7, 414)         =       5.61
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0121
                                                Root MSE          =     .42076

                                  (Std. Err. adjusted for 415 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0292819   .0145323    -2.01   0.045    -.0578481   -.0007157
  exp_highinc |  -.0091186   .0163992    -0.56   0.578    -.0413547    .0231174
   exp_lowinc |  -.0715987   .0175276    -4.08   0.000     -.106053   -.0371445
  exp_migrant |  -.0566694   .0156645    -3.62   0.000    -.0874611   -.0258776
    exp_age50 |  -.0056951   .0149739    -0.38   0.704    -.0351294    .0237393
     exp_male |   -.013231   .0145839    -0.91   0.365    -.0418988    .0154367
exp_co_ethnic |   .0181968   .0137193     1.33   0.185    -.0087714     .045165
        _cons |   .8372283   .0186803    44.82   0.000     .8005082    .8739484
-------------------------------------------------------------------------------

. 
. *Country Pooled Unrestricted (Mixed Land Rights) (1)
. *individual
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border, cluster(sqkm)

Linear regression                               Number of obs     =     12,339
                                                F(8, 815)         =      29.93
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0200
                                                Root MSE          =     .49416

                                  (Std. Err. adjusted for 816 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0196156   .0089937    -2.18   0.029    -.0372692    -.001962
  exp_highinc |   .0088316   .0101847     0.87   0.386    -.0111596    .0288229
   exp_lowinc |  -.0544848   .0115763    -4.71   0.000    -.0772077   -.0317619
  exp_migrant |  -.0961658   .0086959   -11.06   0.000    -.1132347   -.0790968
    exp_age50 |  -.0204848   .0091366    -2.24   0.025    -.0384187   -.0025508
     exp_male |  -.0175243   .0090154    -1.94   0.052    -.0352205    .0001719
exp_co_ethnic |   .0394774   .0088719     4.45   0.000     .0220629    .0568918
   mal_border |  -.0718865   .0107024    -6.72   0.000     -.092894   -.0508791
        _cons |   .6434238   .0133081    48.35   0.000     .6173016     .669546
-------------------------------------------------------------------------------

. *community
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border, cluster(sqkm)

Linear regression                               Number of obs     =     11,874
                                                F(8, 814)         =      10.40
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0076
                                                Root MSE          =     .43676

                                  (Std. Err. adjusted for 815 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0240484   .0086787    -2.77   0.006    -.0410836   -.0070131
  exp_highinc |  -.0138303   .0093203    -1.48   0.138    -.0321249    .0044643
   exp_lowinc |   -.040269   .0096547    -4.17   0.000    -.0592199    -.021318
  exp_migrant |  -.0486827   .0083048    -5.86   0.000     -.064984   -.0323814
    exp_age50 |  -.0161158   .0083724    -1.92   0.055    -.0325497    .0003182
     exp_male |  -.0115982   .0085447    -1.36   0.175    -.0283705    .0051742
exp_co_ethnic |   .0132657   .0075961     1.75   0.081    -.0016445    .0281759
   mal_border |  -.0357842   .0100878    -3.55   0.000    -.0555853   -.0159832
        _cons |   .8217625   .0124224    66.15   0.000     .7973787    .8461462
-------------------------------------------------------------------------------

. 
. *Malawi Unrestricted (Mixed Land Rights) (2)
. *individual
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic if mal_border == 1, cluster(sqkm)

Linear regression                               Number of obs     =      6,789
                                                F(7, 367)         =      15.80
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0145
                                                Root MSE          =     .49666

                                  (Std. Err. adjusted for 368 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0135951   .0122582    -1.11   0.268    -.0377002      .01051
  exp_highinc |   .0083707   .0135005     0.62   0.536    -.0181773    .0349187
   exp_lowinc |  -.0346222   .0148601    -2.33   0.020    -.0638438   -.0054007
  exp_migrant |  -.0925398   .0118207    -7.83   0.000    -.1157845    -.069295
    exp_age50 |   -.031298   .0123667    -2.53   0.012    -.0556165   -.0069795
     exp_male |  -.0243484    .012114    -2.01   0.045      -.04817   -.0005268
exp_co_ethnic |   .0565582   .0117581     4.81   0.000     .0334365      .07968
        _cons |   .5606093   .0168299    33.31   0.000     .5275141    .5937044
-------------------------------------------------------------------------------

. *community; NOTE: this was rounded wrong in previous version of paper; -0.031 ra
> ther than -0.030
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic if mal_border == 1, cluster(sqkm)

Linear regression                               Number of obs     =      6,604
                                                F(7, 367)         =       4.20
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0046
                                                Root MSE          =     .44568

                                  (Std. Err. adjusted for 368 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0304688     .01225    -2.49   0.013    -.0545578   -.0063799
  exp_highinc |  -.0149937   .0127666    -1.17   0.241    -.0400986    .0101113
   exp_lowinc |  -.0031369   .0127207    -0.25   0.805    -.0281515    .0218776
  exp_migrant |  -.0420388   .0108765    -3.87   0.000    -.0634267   -.0206508
    exp_age50 |  -.0214851   .0113866    -1.89   0.060    -.0438762     .000906
     exp_male |  -.0062774   .0114283    -0.55   0.583    -.0287505    .0161958
exp_co_ethnic |   .0179338   .0103001     1.74   0.083    -.0023209    .0381885
        _cons |   .7718776   .0163151    47.31   0.000     .7397949    .8039603
-------------------------------------------------------------------------------

. 
. *Zambia Unrestricted (Mixed Land Rights) (3)
. *individual; NOTE: this was rounded wrong in previous version of paper; -0.027 r
> ather than -0.028
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic if zam_border == 1, cluster(sqkm)

Linear regression                               Number of obs     =      5,550
                                                F(7, 447)         =      14.75
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0181
                                                Root MSE          =      .4908

                                  (Std. Err. adjusted for 448 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0276051   .0132697    -2.08   0.038    -.0536839   -.0015263
  exp_highinc |   .0076326   .0156274     0.49   0.625    -.0230797     .038345
   exp_lowinc |  -.0794144   .0182256    -4.36   0.000     -.115233   -.0435958
  exp_migrant |  -.1011318    .012845    -7.87   0.000    -.1263759   -.0758877
    exp_age50 |  -.0077358   .0135956    -0.57   0.570     -.034455    .0189834
     exp_male |  -.0093768   .0134255    -0.70   0.485    -.0357618    .0170082
exp_co_ethnic |   .0190736   .0135047     1.41   0.159     -.007467    .0456141
        _cons |   .6586312   .0181915    36.21   0.000     .6228798    .6943827
-------------------------------------------------------------------------------

. *community
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic if zam_border == 1, cluster(sqkm)

Linear regression                               Number of obs     =      5,270
                                                F(7, 446)         =       9.36
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0133
                                                Root MSE          =     .42447

                                  (Std. Err. adjusted for 447 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0168298   .0119631    -1.41   0.160    -.0403408    .0066813
  exp_highinc |  -.0140187   .0136045    -1.03   0.303    -.0407556    .0127182
   exp_lowinc |  -.0865792   .0143777    -6.02   0.000    -.1148356   -.0583229
  exp_migrant |  -.0564918    .012874    -4.39   0.000     -.081793   -.0311906
    exp_age50 |  -.0096035   .0123727    -0.78   0.438    -.0339194    .0147125
     exp_male |   -.017973   .0128331    -1.40   0.162    -.0431939     .007248
exp_co_ethnic |   .0074264   .0111905     0.66   0.507    -.0145662     .029419
        _cons |   .8405011   .0158015    53.19   0.000     .8094464    .8715558
-------------------------------------------------------------------------------

. 
. *Ethnicity by village maj/min (15)
. *individual
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_maj_eth mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      5,452
                                                F(8, 609)         =      17.51
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0294
                                                Root MSE          =     .49152

                                 (Std. Err. adjusted for 610 clusters in sqkm)
------------------------------------------------------------------------------
             |               Robust
trust_pay_~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   exp_title |  -.0400618   .0136417    -2.94   0.003    -.0668523   -.0132714
 exp_highinc |   .0200759   .0158164     1.27   0.205    -.0109854    .0511372
  exp_lowinc |  -.0541356   .0182406    -2.97   0.003    -.0899577   -.0183135
 exp_migrant |  -.1030086   .0133751    -7.70   0.000    -.1292756   -.0767417
   exp_age50 |   -.020773   .0140676    -1.48   0.140       -.0484    .0068539
    exp_male |  -.0258584   .0134022    -1.93   0.054    -.0521785    .0004617
 exp_maj_eth |  -.0297486   .0247971    -1.20   0.231    -.0784468    .0189497
  mal_border |   -.109844   .0157164    -6.99   0.000     -.140709   -.0789791
       _cons |   .7035174   .0196518    35.80   0.000     .6649239     .742111
------------------------------------------------------------------------------

. *community
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_maj_eth mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      5,272
                                                F(8, 608)         =       7.78
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0120
                                                Root MSE          =     .43143

                                 (Std. Err. adjusted for 609 clusters in sqkm)
------------------------------------------------------------------------------
             |               Robust
help_colle~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   exp_title |  -.0429292   .0121206    -3.54   0.000    -.0667326   -.0191259
 exp_highinc |  -.0183425   .0140503    -1.31   0.192    -.0459356    .0092506
  exp_lowinc |  -.0477492   .0143118    -3.34   0.001    -.0758558   -.0196425
 exp_migrant |  -.0566006   .0120517    -4.70   0.000    -.0802686   -.0329326
   exp_age50 |  -.0135979   .0123463    -1.10   0.271    -.0378445    .0106487
    exp_male |  -.0132713    .012544    -1.06   0.290    -.0379061    .0113635
 exp_maj_eth |  -.0137934   .0216534    -0.64   0.524     -.056318    .0287312
  mal_border |  -.0455383   .0139843    -3.26   0.001    -.0730018   -.0180748
       _cons |    .858393   .0161831    53.04   0.000     .8266115    .8901746
------------------------------------------------------------------------------

. 
. *Shared identities (16)
. *individual
. reg trust_pay_back exp_title exp_co_wealth exp_co_migrant exp_co_age exp_co_gend
> er ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,213
                                                F(7, 766)         =      21.79
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0232
                                                Root MSE          =      .4927

                                   (Std. Err. adjusted for 767 clusters in sqkm)
--------------------------------------------------------------------------------
               |               Robust
trust_pay_back |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     exp_title |  -.0415815   .0117764    -3.53   0.000    -.0646993   -.0184637
 exp_co_wealth |  -.0120876   .0125203    -0.97   0.335    -.0366658    .0124905
exp_co_migrant |   .0947506   .0121491     7.80   0.000     .0709012    .1186001
    exp_co_age |   .0073332   .0113872     0.64   0.520    -.0150207     .029687
 exp_co_gender |   .0105458   .0112461     0.94   0.349     -.011531    .0326225
 exp_co_ethnic |   .0525006   .0114215     4.60   0.000     .0300794    .0749218
    mal_border |  -.0961903   .0137906    -6.98   0.000    -.1232621   -.0691185
         _cons |   .5324499   .0172219    30.92   0.000     .4986422    .5662576
--------------------------------------------------------------------------------

. *community
. reg help_collect_donations exp_title exp_co_wealth exp_co_migrant exp_co_age exp
> _co_gender ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,950
                                                F(7, 765)         =       6.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0074
                                                Root MSE          =     .43335

                                   (Std. Err. adjusted for 766 clusters in sqkm)
--------------------------------------------------------------------------------
               |               Robust
help_collect~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     exp_title |  -.0321936   .0102834    -3.13   0.002    -.0523807   -.0120065
 exp_co_wealth |   .0042361   .0109468     0.39   0.699    -.0172533    .0257254
exp_co_migrant |   .0442017    .011137     3.97   0.000      .022339    .0660643
    exp_co_age |  -.0097899   .0097161    -1.01   0.314    -.0288633    .0092835
 exp_co_gender |   .0141542   .0105279     1.34   0.179    -.0065128    .0348212
 exp_co_ethnic |   .0248488   .0099837     2.49   0.013     .0052502    .0444474
    mal_border |  -.0395302   .0119704    -3.30   0.001    -.0630289   -.0160315
         _cons |   .7447065   .0145014    51.35   0.000     .7162392    .7731739
--------------------------------------------------------------------------------

. 
. *Shared identities w/respondent characteristics (16)
. *individual; NOTE: this was rounded wrong in previous version of paper; -0.039 r
> ather than -0.040
. reg trust_pay_back exp_title exp_co_wealth exp_co_migrant exp_co_age exp_co_gend
> er ///
> exp_co_ethnic ///
> resp_highinc resp_lowinc resp_migrant age resp_male ///
> resp_lambya resp_lomwe resp_manganja resp_ndali resp_ngoni resp_nkhonde ///
> resp_sena resp_senga resp_sukwa resp_tonga resp_tumbuka resp_yao resp_other ///
> if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,128
                                                F(24, 764)        =       9.83
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0296
                                                Root MSE          =     .49168

                                   (Std. Err. adjusted for 765 clusters in sqkm)
--------------------------------------------------------------------------------
               |               Robust
trust_pay_back |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     exp_title |  -.0396109   .0117499    -3.37   0.001    -.0626768   -.0165449
 exp_co_wealth |  -.0126175   .0124826    -1.01   0.312    -.0371217    .0118867
exp_co_migrant |   .0943721   .0122451     7.71   0.000      .070334    .1184102
    exp_co_age |   .0077956   .0115631     0.67   0.500    -.0149037    .0304948
 exp_co_gender |   .0146501   .0114447     1.28   0.201    -.0078167    .0371169
 exp_co_ethnic |    .052142   .0114498     4.55   0.000     .0296652    .0746187
  resp_highinc |   .0340586   .0175468     1.94   0.053    -.0003871    .0685042
   resp_lowinc |   -.025469   .0136139    -1.87   0.062    -.0521941     .001256
  resp_migrant |   .0052983   .0306506     0.17   0.863    -.0548711    .0654678
           age |  -.0016135   .0003761    -4.29   0.000    -.0023519   -.0008751
     resp_male |   .0640555   .0118733     5.39   0.000     .0407474    .0873637
   resp_lambya |  -.0551783   .0290773    -1.90   0.058    -.1122592    .0019026
    resp_lomwe |  -.0235814   .0605404    -0.39   0.697    -.1424266    .0952639
 resp_manganja |   .1638405   .2188518     0.75   0.454    -.2657818    .5934628
    resp_ndali |   -.178153   .0264998    -6.72   0.000     -.230174    -.126132
    resp_ngoni |  -.0096487   .0213554    -0.45   0.652    -.0515709    .0322736
  resp_nkhonde |  -.0868749   .0444845    -1.95   0.051    -.1742013    .0004515
     resp_sena |   .0502861   .1027025     0.49   0.625    -.1513264    .2518987
    resp_senga |   .0337944   .0783057     0.43   0.666    -.1199255    .1875142
    resp_sukwa |  -.1140165   .0846129    -1.35   0.178    -.2801179    .0520848
    resp_tonga |  -.0649596   .0440411    -1.47   0.141    -.1514155    .0214962
  resp_tumbuka |  -.0673764   .0158591    -4.25   0.000    -.0985091   -.0362437
      resp_yao |  -.0770652   .1060324    -0.73   0.468    -.2852147    .1310843
    resp_other |  -.1046409   .0814452    -1.28   0.199    -.2645238    .0552419
         _cons |   .5520344   .0240877    22.92   0.000     .5047485    .5993202
--------------------------------------------------------------------------------

. *community
. reg help_collect_donations exp_title exp_co_wealth exp_co_migrant exp_co_age exp
> _co_gender ///
> exp_co_ethnic ///
> resp_highinc resp_lowinc resp_migrant age resp_male ///
> resp_lambya resp_lomwe resp_manganja resp_ndali resp_ngoni resp_nkhonde ///
> resp_sena resp_senga resp_sukwa resp_tonga resp_tumbuka resp_yao resp_other ///
> if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,870
                                                F(24, 763)        =       8.06
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0151
                                                Root MSE          =     .43188

                                   (Std. Err. adjusted for 764 clusters in sqkm)
--------------------------------------------------------------------------------
               |               Robust
help_collect~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     exp_title |  -.0299437   .0103786    -2.89   0.004    -.0503177   -.0095697
 exp_co_wealth |   .0035523   .0110084     0.32   0.747    -.0180581    .0251627
exp_co_migrant |   .0455332   .0111525     4.08   0.000       .02364    .0674265
    exp_co_age |  -.0062278   .0098368    -0.63   0.527    -.0255383    .0130826
 exp_co_gender |   .0144049   .0106649     1.35   0.177    -.0065312    .0353411
 exp_co_ethnic |   .0245871   .0100777     2.44   0.015     .0048039    .0443703
  resp_highinc |   .0146667   .0153628     0.95   0.340    -.0154917     .044825
   resp_lowinc |  -.0395761   .0126594    -3.13   0.002    -.0644274   -.0147247
  resp_migrant |  -.0086286   .0275989    -0.31   0.755    -.0628075    .0455503
           age |   .0000479   .0003372     0.14   0.887    -.0006141    .0007099
     resp_male |   .0284173   .0107104     2.65   0.008     .0073919    .0494428
   resp_lambya |  -.0175462   .0314447    -0.56   0.577    -.0792746    .0441823
    resp_lomwe |   .0081526   .0575948     0.14   0.887    -.1049104    .1212156
 resp_manganja |   .2394659   .0286269     8.37   0.000      .183269    .2956628
    resp_ndali |  -.1011683    .033955    -2.98   0.003    -.1678245    -.034512
    resp_ngoni |   .0364103   .0197259     1.85   0.065    -.0023132    .0751338
  resp_nkhonde |  -.0858531   .0397073    -2.16   0.031    -.1638016   -.0079045
     resp_sena |  -.0439615    .085518    -0.51   0.607      -.21184    .1239171
    resp_senga |  -.1305899   .0797062    -1.64   0.102    -.2870593    .0258795
    resp_sukwa |  -.1971102   .0648803    -3.04   0.002    -.3244754   -.0697451
    resp_tonga |  -.0277082   .0307634    -0.90   0.368    -.0880991    .0326827
  resp_tumbuka |  -.0416319   .0144929    -2.87   0.004    -.0700825   -.0131812
      resp_yao |   .0291829   .0799566     0.36   0.715    -.1277782     .186144
    resp_other |   .0854561   .0624776     1.37   0.172    -.0371923    .2081045
         _cons |   .7367886   .0206646    35.65   0.000     .6962223    .7773548
--------------------------------------------------------------------------------

. 
. *Titling treatment x Malawi dummy (18)
. *individual
. reg trust_pay_back exp_title##mal_border exp_highinc exp_lowinc exp_migrant exp_
> age50 exp_male ///
> exp_co_ethnic if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,242
                                                F(9, 767)         =      20.94
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0283
                                                Root MSE          =     .49156

                                  (Std. Err. adjusted for 768 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
  1.exp_title |   -.042595   .0161905    -2.63   0.009    -.0743778   -.0108121
 1.mal_border |  -.0971819   .0176486    -5.51   0.000    -.1318271   -.0625366
              |
    exp_title#|
   mal_border |
         1 1  |   .0006614   .0235249     0.03   0.978    -.0455194    .0468423
              |
  exp_highinc |    .014904   .0134712     1.11   0.269    -.0115408    .0413488
   exp_lowinc |  -.0547148   .0155307    -3.52   0.000    -.0852025    -.024227
  exp_migrant |  -.1011333   .0117846    -8.58   0.000    -.1242671   -.0779995
    exp_age50 |  -.0143611   .0120162    -1.20   0.232    -.0379497    .0092275
     exp_male |  -.0215987   .0114789    -1.88   0.060    -.0441325     .000935
exp_co_ethnic |   .0531242   .0113958     4.66   0.000     .0307537    .0754948
        _cons |   .6657924    .017875    37.25   0.000     .6307028    .7008821
-------------------------------------------------------------------------------

. *community
. reg help_collect_donations exp_title##mal_border exp_highinc exp_lowinc exp_migr
> ant exp_age50 exp_male ///
> exp_co_ethnic if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,975
                                                F(9, 766)         =       7.09
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0094
                                                Root MSE          =     .43309

                                  (Std. Err. adjusted for 767 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
  1.exp_title |  -.0290142   .0145453    -1.99   0.046    -.0575676   -.0004607
 1.mal_border |  -.0375377   .0154682    -2.43   0.015    -.0679027   -.0071727
              |
    exp_title#|
   mal_border |
         1 1  |   -.006789   .0205389    -0.33   0.741    -.0471081    .0335302
              |
  exp_highinc |  -.0140035   .0122439    -1.14   0.253    -.0380391     .010032
   exp_lowinc |  -.0442972   .0124786    -3.55   0.000    -.0687935   -.0198009
  exp_migrant |   -.047052    .010758    -4.37   0.000    -.0681706   -.0259334
    exp_age50 |  -.0154103   .0107597    -1.43   0.152    -.0365323    .0057116
     exp_male |  -.0120727    .010727    -1.13   0.261    -.0331305    .0089852
exp_co_ethnic |   .0232817   .0099756     2.33   0.020      .003699    .0428644
        _cons |   .8265827   .0156164    52.93   0.000     .7959266    .8572388
-------------------------------------------------------------------------------

. 
. 
. 
. *** Figure 2 ***
. 
. *Follow Headman/Woman Mechanism
. reg neigh_follow_hmw exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_
> male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,052
                                                F(8, 761)         =      13.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0152
                                                Root MSE          =     .38017

                                  (Std. Err. adjusted for 762 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
neigh_follo~w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0803396   .0092414    -8.69   0.000    -.0984812    -.062198
  exp_highinc |  -.0291093   .0115418    -2.52   0.012    -.0517669   -.0064518
   exp_lowinc |   .0118699   .0110902     1.07   0.285    -.0099011     .033641
  exp_migrant |  -.0253296   .0086149    -2.94   0.003    -.0422414   -.0084178
    exp_age50 |  -.0003955   .0090364    -0.04   0.965    -.0181347    .0173438
     exp_male |  -.0176777   .0093095    -1.90   0.058     -.035953    .0005976
exp_co_ethnic |   .0025824   .0089319     0.29   0.773    -.0149516    .0201164
   mal_border |  -.0165894    .010892    -1.52   0.128    -.0379713    .0047924
        _cons |   .8961141   .0128169    69.92   0.000     .8709535    .9212747
-------------------------------------------------------------------------------

. est sto pool_hmw

. 
. *Share Well Mechanism
. reg neigh_let_use_well exp_title exp_highinc exp_lowinc exp_migrant exp_age50 ex
> p_male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,945
                                                F(8, 763)         =       4.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0062
                                                Root MSE          =     .37899

                                  (Std. Err. adjusted for 764 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
neigh_let_u~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0391765   .0091519    -4.28   0.000    -.0571424   -.0212106
  exp_highinc |  -.0229255   .0112672    -2.03   0.042    -.0450438   -.0008071
   exp_lowinc |  -.0057779   .0110156    -0.52   0.600    -.0274025    .0158466
  exp_migrant |  -.0331158   .0097552    -3.39   0.001    -.0522661   -.0139656
    exp_age50 |   .0030589   .0092591     0.33   0.741    -.0151175    .0212353
     exp_male |  -.0110241   .0092703    -1.19   0.235    -.0292224    .0071741
exp_co_ethnic |  -.0017691    .008519    -0.21   0.836    -.0184925    .0149543
   mal_border |  -.0194241   .0107076    -1.81   0.070     -.040444    .0015959
        _cons |     .88536   .0132614    66.76   0.000     .8593268    .9113932
-------------------------------------------------------------------------------

. est sto pool_well

. 
. *Observe Way of Life Mechanism
. reg neigh_obs_way_life exp_title exp_highinc exp_lowinc exp_migrant exp_age50 ex
> p_male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,788
                                                F(8, 761)         =      14.66
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0264
                                                Root MSE          =     .46405

                                  (Std. Err. adjusted for 762 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
neigh_obs_w~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0291899   .0122279    -2.39   0.017    -.0531944   -.0051855
  exp_highinc |  -.0196685   .0136295    -1.44   0.149    -.0464245    .0070874
   exp_lowinc |  -.0169827   .0140562    -1.21   0.227    -.0445762    .0106107
  exp_migrant |  -.0326579   .0119234    -2.74   0.006    -.0560645   -.0092513
    exp_age50 |  -.0136181   .0112772    -1.21   0.228    -.0357562      .00852
     exp_male |   .0053082   .0115618     0.46   0.646    -.0173887     .028005
exp_co_ethnic |    .026944   .0115005     2.34   0.019     .0043675    .0495205
   mal_border |   .1409689   .0155009     9.09   0.000     .1105393    .1713985
        _cons |   .6332113   .0194873    32.49   0.000      .594956    .6714666
-------------------------------------------------------------------------------

. est sto pool_obs

. 
. *plot
. coefplot pool_hmw pool_well pool_obs, drop(_cons) xline(0) omitted baselevels ms
> ymbol(d)  ///
> levels(95)  xtitle({bf:Estimated Effect on Mechanism}, size(small)) ///
> xscale(range(-.15 .15)) xlabel(-.15(.05).15) ///
> headings(exp_title = "{bf:Title}" exp_highinc = "{bf:Income}" exp_migrant = "{bf
> :Migration Status}" exp_age50 = "{bf:Age}" exp_male = "{bf:Gender}" exp_co_ethni
> c = "{bf:Ethnicity}" mal_border = "{bf:Sample}", labsize(2.5)) ///
> graphregion(color(white)) grid(between glcolor(white)) legend(label(1 "Success I
> ndex") row(1)  size(small)) ylab(, labs(2.5)) xlab(, labs(2.5))

. *graph save pool_mech.gph, replace
. *graph export pool_mech.pdf, replace
. 
. 
. ********** Appendices **********
. 
. *** Appendix B ***
. 
. *Table B.1
. *outcome variables
. sum trust_pay_back help_collect_donations neigh_follow_hmw neigh_obs_way_life ne
> igh_let_use_well

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
trust_pay_~k |     12,339    .5318908    .4990022          0          1
help_colle~s |     11,874     .740694    .4382723          0          1
neigh_foll~w |     11,966    .8228314    .3818273          0          1
neigh_obs_~e |     11,546    .6721808    .4694389          0          1
neigh_let_~l |     11,812    .8225533    .3820624          0          1

. *treatments
. sum exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_male exp_co_ethni
> c ///
> exp_maj_eth exp_co_wealth exp_co_migrant exp_co_age exp_co_gender

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   exp_title |     12,362    .4936903    .4999804          0          1
 exp_highinc |     12,362    .3357062    .4722558          0          1
  exp_lowinc |     12,362     .327536    .4693336          0          1
 exp_migrant |     12,362    .5049345    .4999959          0          1
   exp_age50 |     12,362    .4953082    .4999982          0          1
-------------+---------------------------------------------------------
    exp_male |     12,362    .4964407    .5000076          0          1
exp_co_eth~c |     12,362    .5064714    .4999783          0          1
 exp_maj_eth |      8,918    .0854452    .2795587          0          1
exp_co_wea~h |     12,258    .3323544    .4710765          0          1
exp_co_mig~t |     12,345    .4942082    .4999867          0          1
-------------+---------------------------------------------------------
  exp_co_age |     12,362    .4974114    .5000135          0          1
exp_co_gen~r |     12,362    .5024268    .5000143          0          1

. *controls
. sum resp_highinc resp_lowinc resp_migrant age resp_male elf

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
resp_highinc |     12,258    .1811878    .3851894          0          1
 resp_lowinc |     12,258    .3957416    .4890293          0          1
resp_migrant |     12,345    .0818955    .2742166          0          1
         age |     12,248     36.5378    15.71864         18         99
   resp_male |     12,362    .3847274    .4865505          0          1
-------------+---------------------------------------------------------
         elf |     10,771    .4257707    .2321269          0   .8656362

. 
. *Figure B.1
. *see "figureb1.do"
. 
. *Table B.2
. *Individ. (OLS)
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,242
                                                F(8, 767)         =      23.39
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0283
                                                Root MSE          =     .49153

                                  (Std. Err. adjusted for 768 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0422636   .0117693    -3.59   0.000    -.0653674   -.0191598
  exp_highinc |   .0149003   .0134586     1.11   0.269    -.0115198    .0413203
   exp_lowinc |  -.0547126    .015533    -3.52   0.000    -.0852049   -.0242204
  exp_migrant |  -.1011336   .0117836    -8.58   0.000    -.1242655   -.0780018
    exp_age50 |  -.0143643    .012015    -1.20   0.232    -.0379505    .0092219
     exp_male |  -.0216041   .0114617    -1.88   0.060    -.0441042     .000896
exp_co_ethnic |   .0531214   .0114015     4.66   0.000     .0307395    .0755033
   mal_border |  -.0968534   .0137065    -7.07   0.000      -.12376   -.0699467
        _cons |    .665635   .0174502    38.14   0.000     .6313791    .6998909
-------------------------------------------------------------------------------

. *Individ. (logit)
. logit trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_
> male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -4995.3841  
Iteration 1:   log pseudolikelihood = -4891.8852  
Iteration 2:   log pseudolikelihood =   -4891.81  
Iteration 3:   log pseudolikelihood =   -4891.81  

Logistic regression                             Number of obs     =      7,242
                                                Wald chi2(8)      =     171.11
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =   -4891.81               Pseudo R2         =     0.0207

                                   (Std. Err. adjusted for 768 clusters in sqkm)
--------------------------------------------------------------------------------
               |               Robust
trust_pay_back |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     exp_title |  -.1749172   .0487455    -3.59   0.000    -.2704566   -.0793778
   exp_highinc |   .0618978   .0560145     1.11   0.269    -.0478887    .1716843
    exp_lowinc |  -.2256321   .0640721    -3.52   0.000    -.3512111   -.1000531
   exp_migrant |  -.4167044   .0488076    -8.54   0.000    -.5123656   -.3210432
     exp_age50 |  -.0595668   .0497809    -1.20   0.231    -.1571356    .0380021
      exp_male |  -.0895723   .0474597    -1.89   0.059    -.1825915    .0034469
 exp_co_ethnic |   .2197955   .0471919     4.66   0.000      .127301      .31229
    mal_border |  -.3992055   .0568698    -7.02   0.000    -.5106683   -.2877427
         _cons |   .6830284    .073831     9.25   0.000     .5383223    .8277345
--------------------------------------------------------------------------------

. *Comm. (OLS)
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,975
                                                F(8, 766)         =       7.97
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0094
                                                Root MSE          =     .43306

                                  (Std. Err. adjusted for 767 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0324375   .0102568    -3.16   0.002    -.0525722   -.0123029
  exp_highinc |  -.0139679   .0122484    -1.14   0.254    -.0380124    .0100766
   exp_lowinc |  -.0443234   .0124814    -3.55   0.000    -.0688252   -.0198215
  exp_migrant |  -.0470549   .0107545    -4.38   0.000    -.0681666   -.0259432
    exp_age50 |   -.015369   .0107509    -1.43   0.153    -.0364737    .0057358
     exp_male |  -.0120076   .0107347    -1.12   0.264    -.0330805    .0090652
exp_co_ethnic |     .02331   .0099701     2.34   0.020      .003738     .042882
   mal_border |  -.0409129   .0118696    -3.45   0.001    -.0642137   -.0176121
        _cons |    .828205   .0149902    55.25   0.000     .7987782    .8576319
-------------------------------------------------------------------------------

. *Comm. (logit)
. logit help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_ag
> e50 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Iteration 0:   log pseudolikelihood =  -3946.543  
Iteration 1:   log pseudolikelihood = -3913.6342  
Iteration 2:   log pseudolikelihood = -3913.5329  
Iteration 3:   log pseudolikelihood = -3913.5329  

Logistic regression                             Number of obs     =      6,975
                                                Wald chi2(8)      =      61.92
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -3913.5329               Pseudo R2         =     0.0084

                                    (Std. Err. adjusted for 767 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
help_collect_~s |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      exp_title |  -.1730316   .0547829    -3.16   0.002    -.2804041   -.0656591
    exp_highinc |  -.0769848   .0673111    -1.14   0.253     -.208912    .0549425
     exp_lowinc |  -.2349724   .0657872    -3.57   0.000    -.3639129   -.1060318
    exp_migrant |  -.2513174   .0578265    -4.35   0.000    -.3646554   -.1379795
      exp_age50 |  -.0818551   .0574313    -1.43   0.154    -.1944184    .0307083
       exp_male |   -.064667   .0572141    -1.13   0.258    -.1768045    .0474706
  exp_co_ethnic |   .1242081   .0532776     2.33   0.020     .0197859    .2286302
     mal_border |  -.2186999   .0635739    -3.44   0.001    -.3433024   -.0940974
          _cons |   1.529316   .0856187    17.86   0.000     1.361506    1.697126
---------------------------------------------------------------------------------

. 
. *Table B.3
. * Verticle (Follow Headman/Woman Mechanism)
. reg neigh_follow_hmw exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_
> male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,052
                                                F(8, 761)         =      13.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0152
                                                Root MSE          =     .38017

                                  (Std. Err. adjusted for 762 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
neigh_follo~w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0803396   .0092414    -8.69   0.000    -.0984812    -.062198
  exp_highinc |  -.0291093   .0115418    -2.52   0.012    -.0517669   -.0064518
   exp_lowinc |   .0118699   .0110902     1.07   0.285    -.0099011     .033641
  exp_migrant |  -.0253296   .0086149    -2.94   0.003    -.0422414   -.0084178
    exp_age50 |  -.0003955   .0090364    -0.04   0.965    -.0181347    .0173438
     exp_male |  -.0176777   .0093095    -1.90   0.058     -.035953    .0005976
exp_co_ethnic |   .0025824   .0089319     0.29   0.773    -.0149516    .0201164
   mal_border |  -.0165894    .010892    -1.52   0.128    -.0379713    .0047924
        _cons |   .8961141   .0128169    69.92   0.000     .8709535    .9212747
-------------------------------------------------------------------------------

. *Horizontal (Share Well Mechanism)
. reg neigh_let_use_well exp_title exp_highinc exp_lowinc exp_migrant exp_age50 ex
> p_male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,945
                                                F(8, 763)         =       4.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0062
                                                Root MSE          =     .37899

                                  (Std. Err. adjusted for 764 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
neigh_let_u~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0391765   .0091519    -4.28   0.000    -.0571424   -.0212106
  exp_highinc |  -.0229255   .0112672    -2.03   0.042    -.0450438   -.0008071
   exp_lowinc |  -.0057779   .0110156    -0.52   0.600    -.0274025    .0158466
  exp_migrant |  -.0331158   .0097552    -3.39   0.001    -.0522661   -.0139656
    exp_age50 |   .0030589   .0092591     0.33   0.741    -.0151175    .0212353
     exp_male |  -.0110241   .0092703    -1.19   0.235    -.0292224    .0071741
exp_co_ethnic |  -.0017691    .008519    -0.21   0.836    -.0184925    .0149543
   mal_border |  -.0194241   .0107076    -1.81   0.070     -.040444    .0015959
        _cons |     .88536   .0132614    66.76   0.000     .8593268    .9113932
-------------------------------------------------------------------------------

. *Diffuse (Observe Way of Life Mechanism)
. reg neigh_obs_way_life exp_title exp_highinc exp_lowinc exp_migrant exp_age50 ex
> p_male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,788
                                                F(8, 761)         =      14.66
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0264
                                                Root MSE          =     .46405

                                  (Std. Err. adjusted for 762 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
neigh_obs_w~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0291899   .0122279    -2.39   0.017    -.0531944   -.0051855
  exp_highinc |  -.0196685   .0136295    -1.44   0.149    -.0464245    .0070874
   exp_lowinc |  -.0169827   .0140562    -1.21   0.227    -.0445762    .0106107
  exp_migrant |  -.0326579   .0119234    -2.74   0.006    -.0560645   -.0092513
    exp_age50 |  -.0136181   .0112772    -1.21   0.228    -.0357562      .00852
     exp_male |   .0053082   .0115618     0.46   0.646    -.0173887     .028005
exp_co_ethnic |    .026944   .0115005     2.34   0.019     .0043675    .0495205
   mal_border |   .1409689   .0155009     9.09   0.000     .1105393    .1713985
        _cons |   .6332113   .0194873    32.49   0.000      .594956    .6714666
-------------------------------------------------------------------------------

. 
. 
. *** Appendix C ***
. 
. *Table C.1
. *Malawi Individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic if mal_border == 1 & have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      3,629
                                                F(7, 351)         =      12.31
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0210
                                                Root MSE          =     .49524

                                  (Std. Err. adjusted for 352 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0427326   .0171256    -2.50   0.013    -.0764143   -.0090509
  exp_highinc |   .0168175   .0181457     0.93   0.355    -.0188705    .0525054
   exp_lowinc |  -.0357235   .0215272    -1.66   0.098     -.078062     .006615
  exp_migrant |  -.0943327   .0166392    -5.67   0.000    -.1270579   -.0616076
    exp_age50 |   -.028412   .0177472    -1.60   0.110    -.0633162    .0064922
     exp_male |  -.0332421   .0166428    -2.00   0.047    -.0659743   -.0005099
exp_co_ethnic |     .08253   .0158704     5.20   0.000     .0513169    .1137431
        _cons |   .5568018   .0241914    23.02   0.000     .5092235    .6043801
-------------------------------------------------------------------------------

. *Malawi Comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic if mal_border == 1 & have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      3,518
                                                F(7, 351)         =       3.04
                                                Prob > F          =     0.0041
                                                R-squared         =     0.0057
                                                Root MSE          =     .44468

                                  (Std. Err. adjusted for 352 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0364252   .0144547    -2.52   0.012    -.0648539   -.0079965
  exp_highinc |  -.0199611   .0181336    -1.10   0.272    -.0556252    .0157031
   exp_lowinc |  -.0176647   .0175297    -1.01   0.314    -.0521413    .0168118
  exp_migrant |  -.0374095   .0147659    -2.53   0.012    -.0664502   -.0083688
    exp_age50 |   -.024602   .0154388    -1.59   0.112    -.0549662    .0057622
     exp_male |  -.0110034   .0157837    -0.70   0.486     -.042046    .0200392
exp_co_ethnic |   .0284026   .0144291     1.97   0.050     .0000242    .0567811
        _cons |   .7793337   .0209833    37.14   0.000     .7380649    .8206026
-------------------------------------------------------------------------------

. *Zambia Individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic if mal_border == 0 & have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      3,613
                                                F(7, 415)         =       9.90
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0208
                                                Root MSE          =     .48745

                                  (Std. Err. adjusted for 416 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0422743   .0161778    -2.61   0.009     -.074075   -.0104735
  exp_highinc |   .0112796    .020049     0.56   0.574    -.0281305    .0506898
   exp_lowinc |  -.0744642   .0222517    -3.35   0.001    -.1182044   -.0307241
  exp_migrant |  -.1085957    .016593    -6.54   0.000    -.1412126   -.0759789
    exp_age50 |  -.0003184   .0161446    -0.02   0.984    -.0320538     .031417
     exp_male |  -.0105828   .0157532    -0.67   0.502    -.0415488    .0203831
exp_co_ethnic |   .0239004   .0160626     1.49   0.138    -.0076737    .0554745
        _cons |   .6796638   .0216294    31.42   0.000     .6371471    .7221806
-------------------------------------------------------------------------------

. *Zambia Comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic if mal_border == 0 & have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      3,457
                                                F(7, 414)         =       5.61
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0121
                                                Root MSE          =     .42076

                                  (Std. Err. adjusted for 415 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0292819   .0145323    -2.01   0.045    -.0578481   -.0007157
  exp_highinc |  -.0091186   .0163992    -0.56   0.578    -.0413547    .0231174
   exp_lowinc |  -.0715987   .0175276    -4.08   0.000     -.106053   -.0371445
  exp_migrant |  -.0566694   .0156645    -3.62   0.000    -.0874611   -.0258776
    exp_age50 |  -.0056951   .0149739    -0.38   0.704    -.0351294    .0237393
     exp_male |   -.013231   .0145839    -0.91   0.365    -.0418988    .0154367
exp_co_ethnic |   .0181968   .0137193     1.33   0.185    -.0087714     .045165
        _cons |   .8372283   .0186803    44.82   0.000     .8005082    .8739484
-------------------------------------------------------------------------------

. 
. *Table C.2
. *Individ. (OLS)
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border, cluster(sqkm)

Linear regression                               Number of obs     =     12,339
                                                F(8, 815)         =      29.93
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0200
                                                Root MSE          =     .49416

                                  (Std. Err. adjusted for 816 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0196156   .0089937    -2.18   0.029    -.0372692    -.001962
  exp_highinc |   .0088316   .0101847     0.87   0.386    -.0111596    .0288229
   exp_lowinc |  -.0544848   .0115763    -4.71   0.000    -.0772077   -.0317619
  exp_migrant |  -.0961658   .0086959   -11.06   0.000    -.1132347   -.0790968
    exp_age50 |  -.0204848   .0091366    -2.24   0.025    -.0384187   -.0025508
     exp_male |  -.0175243   .0090154    -1.94   0.052    -.0352205    .0001719
exp_co_ethnic |   .0394774   .0088719     4.45   0.000     .0220629    .0568918
   mal_border |  -.0718865   .0107024    -6.72   0.000     -.092894   -.0508791
        _cons |   .6434238   .0133081    48.35   0.000     .6173016     .669546
-------------------------------------------------------------------------------

. *Individ. (logit)
. logit trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_
> male ///
> exp_co_ethnic mal_border, cluster(sqkm)

Iteration 0:   log pseudolikelihood =  -8527.628  
Iteration 1:   log pseudolikelihood = -8403.4636  
Iteration 2:   log pseudolikelihood = -8403.4214  
Iteration 3:   log pseudolikelihood = -8403.4214  

Logistic regression                             Number of obs     =     12,339
                                                Wald chi2(8)      =     224.99
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -8403.4214               Pseudo R2         =     0.0146

                                   (Std. Err. adjusted for 816 clusters in sqkm)
--------------------------------------------------------------------------------
               |               Robust
trust_pay_back |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     exp_title |   -.080291   .0368475    -2.18   0.029    -.1525108   -.0080713
   exp_highinc |   .0363018   .0418616     0.87   0.386    -.0457454    .1183491
    exp_lowinc |  -.2223779   .0472912    -4.70   0.000    -.3150668   -.1296889
   exp_migrant |  -.3918625    .035629   -11.00   0.000    -.4616941   -.3220309
     exp_age50 |  -.0840102   .0374329    -2.24   0.025    -.1573774   -.0106429
      exp_male |  -.0718538   .0369178    -1.95   0.052    -.1442114    .0005039
 exp_co_ethnic |   .1616024   .0363624     4.44   0.000     .0903334    .2328713
    mal_border |  -.2940317   .0439511    -6.69   0.000    -.3801744   -.2078891
         _cons |   .5857225   .0553852    10.58   0.000     .4771694    .6942756
--------------------------------------------------------------------------------

. *Comm. (OLS)
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border, cluster(sqkm)

Linear regression                               Number of obs     =     11,874
                                                F(8, 814)         =      10.40
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0076
                                                Root MSE          =     .43676

                                  (Std. Err. adjusted for 815 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0240484   .0086787    -2.77   0.006    -.0410836   -.0070131
  exp_highinc |  -.0138303   .0093203    -1.48   0.138    -.0321249    .0044643
   exp_lowinc |   -.040269   .0096547    -4.17   0.000    -.0592199    -.021318
  exp_migrant |  -.0486827   .0083048    -5.86   0.000     -.064984   -.0323814
    exp_age50 |  -.0161158   .0083724    -1.92   0.055    -.0325497    .0003182
     exp_male |  -.0115982   .0085447    -1.36   0.175    -.0283705    .0051742
exp_co_ethnic |   .0132657   .0075961     1.75   0.081    -.0016445    .0281759
   mal_border |  -.0357842   .0100878    -3.55   0.000    -.0555853   -.0159832
        _cons |   .8217625   .0124224    66.15   0.000     .7973787    .8461462
-------------------------------------------------------------------------------

. *Comm. (logit)
. logit help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_ag
> e50 exp_male ///
> exp_co_ethnic mal_border, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -6795.8442  
Iteration 1:   log pseudolikelihood = -6750.8444  
Iteration 2:   log pseudolikelihood = -6750.7392  
Iteration 3:   log pseudolikelihood = -6750.7392  

Logistic regression                             Number of obs     =     11,874
                                                Wald chi2(8)      =      81.58
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -6750.7392               Pseudo R2         =     0.0066

                                    (Std. Err. adjusted for 815 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
help_collect_~s |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      exp_title |  -.1259047   .0455795    -2.76   0.006     -.215239   -.0365705
    exp_highinc |  -.0745993   .0500646    -1.49   0.136     -.172724    .0235255
     exp_lowinc |  -.2101546   .0502917    -4.18   0.000    -.3087245   -.1115847
    exp_migrant |  -.2553114   .0435862    -5.86   0.000    -.3407387    -.169884
      exp_age50 |  -.0846324   .0439234    -1.93   0.054    -.1707207    .0014559
       exp_male |  -.0609755   .0448026    -1.36   0.174    -.1487869     .026836
  exp_co_ethnic |    .069296   .0398689     1.74   0.082    -.0088455    .1474375
     mal_border |  -.1887586   .0533973    -3.53   0.000    -.2934153   -.0841019
          _cons |   1.485429   .0693701    21.41   0.000     1.349466    1.621392
---------------------------------------------------------------------------------

. 
. *Table C.3
. *Malawi full models
. *Individ. (OLS)
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_lambya exp_lomwe exp_ndali exp_ngoni exp_senga exp_tumbuka ///
> exp_yao if mal_border == 1, cluster(sqkm)

Linear regression                               Number of obs     =      6,789
                                                F(13, 367)        =       7.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0126
                                                Root MSE          =     .49737

                                 (Std. Err. adjusted for 368 clusters in sqkm)
------------------------------------------------------------------------------
             |               Robust
trust_pay_~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   exp_title |  -.0137992   .0122594    -1.13   0.261    -.0379066    .0103082
 exp_highinc |   .0085346    .013476     0.63   0.527    -.0179653    .0350345
  exp_lowinc |  -.0349925   .0148417    -2.36   0.019    -.0641781    -.005807
 exp_migrant |  -.0926023   .0118106    -7.84   0.000    -.1158273   -.0693773
   exp_age50 |  -.0303141   .0123514    -2.45   0.015    -.0546024   -.0060258
    exp_male |  -.0242374   .0122261    -1.98   0.048    -.0482794   -.0001955
  exp_lambya |  -.0097454   .0256256    -0.38   0.704    -.0601368    .0406459
   exp_lomwe |   .0059429   .0260334     0.23   0.820    -.0452504    .0571361
   exp_ndali |  -.0294045   .0233036    -1.26   0.208    -.0752298    .0164208
   exp_ngoni |   -.015647   .0241847    -0.65   0.518     -.063205    .0319109
   exp_senga |  -.0514728   .0230951    -2.23   0.026    -.0968882   -.0060574
 exp_tumbuka |  -.0356291   .0241547    -1.48   0.141    -.0831282    .0118699
     exp_yao |  -.0155498   .0246463    -0.63   0.528    -.0640155    .0329159
       _cons |    .607923   .0235853    25.78   0.000     .5615438    .6543023
------------------------------------------------------------------------------

. *Individ. (logit)
. logit trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_
> male ///
> exp_lambya exp_lomwe exp_ndali exp_ngoni exp_senga exp_tumbuka ///
> exp_yao if mal_border == 1, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -4705.7761  
Iteration 1:   log pseudolikelihood = -4662.9886  
Iteration 2:   log pseudolikelihood = -4662.9851  
Iteration 3:   log pseudolikelihood = -4662.9851  

Logistic regression                             Number of obs     =      6,789
                                                Wald chi2(13)     =      94.91
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -4662.9851               Pseudo R2         =     0.0091

                                   (Std. Err. adjusted for 368 clusters in sqkm)
--------------------------------------------------------------------------------
               |               Robust
trust_pay_back |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     exp_title |  -.0558823   .0496112    -1.13   0.260    -.1531183    .0413538
   exp_highinc |   .0345224   .0545036     0.63   0.526    -.0723027    .1413475
    exp_lowinc |  -.1416956   .0600503    -2.36   0.018    -.2593921   -.0239992
   exp_migrant |  -.3731028   .0478076    -7.80   0.000     -.466804   -.2794016
     exp_age50 |  -.1227693   .0500065    -2.46   0.014    -.2207802   -.0247583
      exp_male |  -.0981607   .0494716    -1.98   0.047    -.1951233   -.0011981
    exp_lambya |  -.0394872   .1036332    -0.38   0.703    -.2426045    .1636301
     exp_lomwe |   .0240727   .1053747     0.23   0.819    -.1824579    .2306034
     exp_ndali |  -.1190191   .0942812    -1.26   0.207    -.3038068    .0657685
     exp_ngoni |  -.0633581   .0978156    -0.65   0.517    -.2550731    .1283569
     exp_senga |   -.208454   .0935515    -2.23   0.026    -.3918116   -.0250964
   exp_tumbuka |  -.1442815   .0977667    -1.48   0.140    -.3359008    .0473377
       exp_yao |  -.0629975    .099706    -0.63   0.527    -.2584177    .1324226
         _cons |   .4360541   .0957686     4.55   0.000     .2483511    .6237572
--------------------------------------------------------------------------------

. *Comm. (OLS)
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_lambya exp_lomwe exp_ndali exp_ngoni exp_senga exp_tumbuka ///
> exp_yao if mal_border == 1, cluster(sqkm)

Linear regression                               Number of obs     =      6,604
                                                F(13, 367)        =       2.98
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0065
                                                Root MSE          =     .44545

                                 (Std. Err. adjusted for 368 clusters in sqkm)
------------------------------------------------------------------------------
             |               Robust
help_colle~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   exp_title |   -.030908   .0122599    -2.52   0.012    -.0550164   -.0067996
 exp_highinc |    -.01494   .0126713    -1.18   0.239    -.0398575    .0099775
  exp_lowinc |  -.0032404   .0127732    -0.25   0.800    -.0283582    .0218775
 exp_migrant |  -.0421215    .010832    -3.89   0.000    -.0634221   -.0208209
   exp_age50 |  -.0214528   .0114332    -1.88   0.061    -.0439356      .00103
    exp_male |  -.0061851   .0113916    -0.54   0.587     -.028586    .0162158
  exp_lambya |   .0030055   .0239227     0.13   0.900    -.0440373    .0500483
   exp_lomwe |   .0369499   .0213098     1.73   0.084    -.0049548    .0788545
   exp_ndali |  -.0332365   .0230156    -1.44   0.150    -.0784956    .0120225
   exp_ngoni |    .002217    .022503     0.10   0.922     -.042034    .0464679
   exp_senga |   -.027719   .0227332    -1.22   0.224    -.0724227    .0169848
 exp_tumbuka |   .0148177   .0236262     0.63   0.531    -.0316421    .0612775
     exp_yao |  -.0103596   .0229229    -0.45   0.652    -.0554363    .0347171
       _cons |   .7834885   .0224393    34.92   0.000     .7393627    .8276142
------------------------------------------------------------------------------

. *Comm. (logit)
. logit help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_ag
> e50 exp_male ///
> exp_lambya exp_lomwe exp_ndali exp_ngoni exp_senga exp_tumbuka ///
> exp_yao if mal_border == 1, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -3883.1998  
Iteration 1:   log pseudolikelihood = -3861.6715  
Iteration 2:   log pseudolikelihood = -3861.6292  
Iteration 3:   log pseudolikelihood = -3861.6292  

Logistic regression                             Number of obs     =      6,604
                                                Wald chi2(13)     =      38.58
                                                Prob > chi2       =     0.0002
Log pseudolikelihood = -3861.6292               Pseudo R2         =     0.0056

                                    (Std. Err. adjusted for 368 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
help_collect_~s |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      exp_title |  -.1557765   .0620467    -2.51   0.012    -.2773858   -.0341672
    exp_highinc |  -.0754975   .0635063    -1.19   0.235    -.1999676    .0489725
     exp_lowinc |  -.0162342   .0650995    -0.25   0.803    -.1438268    .1113584
    exp_migrant |  -.2125034   .0544306    -3.90   0.000    -.3191854   -.1058215
      exp_age50 |   -.108383   .0578034    -1.88   0.061    -.2216755    .0049096
       exp_male |  -.0310318   .0574243    -0.54   0.589    -.1435813    .0815178
     exp_lambya |   .0150449   .1211192     0.12   0.901    -.2223443    .2524341
      exp_lomwe |   .1955297   .1120197     1.75   0.081    -.0240249    .4150844
      exp_ndali |  -.1627515   .1131482    -1.44   0.150     -.384518     .059015
      exp_ngoni |   .0111316   .1139215     0.10   0.922    -.2121504    .2344137
      exp_senga |  -.1362443   .1120384    -1.22   0.224    -.3558356     .083347
    exp_tumbuka |   .0770494   .1214899     0.63   0.526    -.1610664    .3151652
        exp_yao |   -.052198   .1147717    -0.45   0.649    -.2771465    .1727505
          _cons |   1.269264   .1158996    10.95   0.000     1.042105    1.496423
---------------------------------------------------------------------------------

. 
. 
. *Table C.4 
. *Zambia full models
. *Individ (OLS)
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_namwanga exp_bemba exp_senga exp_ngoni exp_chewa exp_tumbuka ///
> exp_nyika if zam_border == 1, cluster(sqkm)

Linear regression                               Number of obs     =      5,550
                                                F(13, 447)        =       8.53
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0184
                                                Root MSE          =     .49097

                                 (Std. Err. adjusted for 448 clusters in sqkm)
------------------------------------------------------------------------------
             |               Robust
trust_pay_~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   exp_title |  -.0267784   .0132538    -2.02   0.044    -.0528258   -.0007309
 exp_highinc |   .0072711   .0156766     0.46   0.643    -.0235379    .0380801
  exp_lowinc |  -.0795735   .0182058    -4.37   0.000    -.1153532   -.0437938
 exp_migrant |  -.1008708   .0128717    -7.84   0.000    -.1261673   -.0755742
   exp_age50 |  -.0077947   .0135808    -0.57   0.566    -.0344848    .0188955
    exp_male |  -.0099332   .0133772    -0.74   0.458    -.0362233    .0163569
exp_namwanga |   .0178754   .0251861     0.71   0.478    -.0316224    .0673733
   exp_bemba |   .0475231    .025448     1.87   0.062    -.0024895    .0975357
   exp_senga |   .0255952   .0260974     0.98   0.327    -.0256936     .076884
   exp_ngoni |   .0340361   .0245174     1.39   0.166    -.0141476    .0822199
   exp_chewa |   .0333767   .0246827     1.35   0.177    -.0151318    .0818852
 exp_tumbuka |   .0186851   .0258762     0.72   0.471    -.0321691    .0695392
   exp_nyika |   .0193824    .025995     0.75   0.456    -.0317052    .0704701
       _cons |   .6439558   .0237483    27.12   0.000     .5972836     .690628
------------------------------------------------------------------------------

. *Individ (logit)
. logit trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_
> male ///
> exp_namwanga exp_bemba exp_senga exp_ngoni exp_chewa exp_tumbuka ///
> exp_nyika if zam_border == 1, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -3790.8365  
Iteration 1:   log pseudolikelihood = -3739.4458  
Iteration 2:   log pseudolikelihood = -3739.4213  
Iteration 3:   log pseudolikelihood = -3739.4213  

Logistic regression                             Number of obs     =      5,550
                                                Wald chi2(13)     =     105.81
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -3739.4213               Pseudo R2         =     0.0136

                                   (Std. Err. adjusted for 448 clusters in sqkm)
--------------------------------------------------------------------------------
               |               Robust
trust_pay_back |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     exp_title |  -.1113874   .0550418    -2.02   0.043    -.2192673   -.0035075
   exp_highinc |   .0304153   .0659664     0.46   0.645    -.0988765    .1597072
    exp_lowinc |  -.3271799    .075097    -4.36   0.000    -.4743674   -.1799924
   exp_migrant |  -.4169417   .0534833    -7.80   0.000    -.5217671   -.3121164
     exp_age50 |  -.0323641   .0564033    -0.57   0.566    -.1429125    .0781843
      exp_male |  -.0413349   .0555416    -0.74   0.457    -.1501945    .0675246
  exp_namwanga |   .0731722   .1037204     0.71   0.481    -.1301159    .2764604
     exp_bemba |   .1969082   .1057893     1.86   0.063    -.0104351    .4042514
     exp_senga |   .1055176   .1076542     0.98   0.327    -.1054807     .316516
     exp_ngoni |   .1409162   .1015036     1.39   0.165    -.0580272    .3398596
     exp_chewa |   .1382555   .1024495     1.35   0.177    -.0625419    .3390529
   exp_tumbuka |   .0766203   .1066293     0.72   0.472    -.1323692    .2856098
     exp_nyika |   .0798935    .107073     0.75   0.456    -.1299657    .2897527
         _cons |   .5926748   .0991497     5.98   0.000      .398345    .7870046
--------------------------------------------------------------------------------

. *Comm. (OLS)
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_namwanga exp_bemba exp_senga exp_ngoni exp_chewa exp_tumbuka ///
> exp_nyika if zam_border == 1, cluster(sqkm)

Linear regression                               Number of obs     =      5,270
                                                F(13, 446)        =       6.00
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0155
                                                Root MSE          =     .42424

                                 (Std. Err. adjusted for 447 clusters in sqkm)
------------------------------------------------------------------------------
             |               Robust
help_colle~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   exp_title |  -.0170306    .011926    -1.43   0.154    -.0404687    .0064075
 exp_highinc |  -.0140626   .0136171    -1.03   0.302    -.0408243    .0126991
  exp_lowinc |  -.0875142   .0143554    -6.10   0.000    -.1157269   -.0593016
 exp_migrant |  -.0560452   .0129331    -4.33   0.000    -.0814626   -.0306277
   exp_age50 |  -.0095509   .0123958    -0.77   0.441    -.0339124    .0148105
    exp_male |  -.0190914   .0128714    -1.48   0.139    -.0443876    .0062049
exp_namwanga |  -.0126322   .0226981    -0.56   0.578    -.0572406    .0319762
   exp_bemba |   .0145332   .0236785     0.61   0.540    -.0320021    .0610685
   exp_senga |   .0071286   .0224824     0.32   0.751    -.0370559    .0513131
   exp_ngoni |  -.0478276   .0235181    -2.03   0.043    -.0940477   -.0016076
   exp_chewa |   .0194162   .0226187     0.86   0.391    -.0250363    .0638687
 exp_tumbuka |  -.0190968   .0213341    -0.90   0.371    -.0610247    .0228311
   exp_nyika |   -.006288   .0222259    -0.28   0.777    -.0499684    .0373925
       _cons |   .8507766   .0208041    40.89   0.000     .8098904    .8916629
------------------------------------------------------------------------------

. *Comm. (logit)
. logit help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_ag
> e50 exp_male ///
> exp_namwanga exp_bemba exp_senga exp_ngoni exp_chewa exp_tumbuka ///
> exp_nyika if zam_border == 1, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -2903.2687  
Iteration 1:   log pseudolikelihood =  -2862.987  
Iteration 2:   log pseudolikelihood =   -2862.74  
Iteration 3:   log pseudolikelihood =   -2862.74  

Logistic regression                             Number of obs     =      5,270
                                                Wald chi2(13)     =      79.06
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =   -2862.74               Pseudo R2         =     0.0140

                                    (Std. Err. adjusted for 447 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
help_collect_~s |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      exp_title |  -.0946914   .0663835    -1.43   0.154    -.2248007    .0354179
    exp_highinc |  -.0844668   .0815585    -1.04   0.300    -.2443185    .0753848
     exp_lowinc |  -.4751872   .0770822    -6.16   0.000    -.6262656   -.3241089
    exp_migrant |  -.3126028    .072517    -4.31   0.000    -.4547335   -.1704721
      exp_age50 |  -.0533498   .0689337    -0.77   0.439    -.1884573    .0817577
       exp_male |  -.1065723   .0716669    -1.49   0.137    -.2470369    .0338924
   exp_namwanga |  -.0711642   .1259012    -0.57   0.572    -.3179261    .1755977
      exp_bemba |   .0829607   .1358349     0.61   0.541    -.1832709    .3491922
      exp_senga |   .0405324   .1274948     0.32   0.751    -.2093529    .2904176
      exp_ngoni |  -.2539856   .1244931    -2.04   0.041    -.4979875   -.0099836
      exp_chewa |   .1154988    .132251     0.87   0.382    -.1437084     .374706
    exp_tumbuka |  -.1052247   .1169661    -0.90   0.368    -.3344741    .1240247
      exp_nyika |  -.0345024   .1236136    -0.28   0.780    -.2767806    .2077758
          _cons |   1.675274   .1232515    13.59   0.000     1.433706    1.916842
---------------------------------------------------------------------------------

. 
. 
. *Table C.5
. *(7) Sub-sample by high/low ELF
. *low ELF, Individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0 & elf <= .39, cluster(sqkm)

Linear regression                               Number of obs     =      2,989
                                                F(8, 204)         =       9.86
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0256
                                                Root MSE          =     .49199

                                  (Std. Err. adjusted for 205 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0378983   .0181768    -2.08   0.038    -.0737368   -.0020598
  exp_highinc |   .0205178   .0216899     0.95   0.345    -.0222474     .063283
   exp_lowinc |  -.0339931    .025577    -1.33   0.185    -.0844223    .0164361
  exp_migrant |    -.08753    .018087    -4.84   0.000    -.1231915   -.0518685
    exp_age50 |  -.0037812   .0191639    -0.20   0.844    -.0415659    .0340034
     exp_male |  -.0235436   .0188142    -1.25   0.212    -.0606387    .0135516
exp_co_ethnic |    .082352   .0163019     5.05   0.000     .0502101    .1144939
   mal_border |  -.0821319   .0216123    -3.80   0.000    -.1247439   -.0395198
        _cons |   .6313377   .0268766    23.49   0.000      .578346    .6843293
-------------------------------------------------------------------------------

. *high ELF, Individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0 & elf > .39, cluster(sqkm)

Linear regression                               Number of obs     =      4,253
                                                F(8, 562)         =      16.01
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0324
                                                Root MSE          =     .49109

                                  (Std. Err. adjusted for 563 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0437778   .0155149    -2.82   0.005     -.074252   -.0133035
  exp_highinc |   .0111727   .0172312     0.65   0.517    -.0226728    .0450182
   exp_lowinc |  -.0697028   .0193051    -3.61   0.000    -.1076217   -.0317838
  exp_migrant |  -.1112647   .0154133    -7.22   0.000    -.1415393   -.0809901
    exp_age50 |  -.0220692   .0154609    -1.43   0.154    -.0524374     .008299
     exp_male |  -.0198584   .0143005    -1.39   0.165    -.0479474    .0082306
exp_co_ethnic |   .0334706   .0155188     2.16   0.031     .0029888    .0639525
   mal_border |  -.1068996   .0176589    -6.05   0.000     -.141585   -.0722142
        _cons |    .688164   .0227077    30.31   0.000     .6435617    .7327662
-------------------------------------------------------------------------------

. *low ELF, Comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0 & elf <= .39, cluster(sqkm)

Linear regression                               Number of obs     =      2,889
                                                F(8, 204)         =       4.45
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0135
                                                Root MSE          =     .43346

                                  (Std. Err. adjusted for 205 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0421205   .0164088    -2.57   0.011    -.0744731   -.0097679
  exp_highinc |  -.0169108   .0193826    -0.87   0.384    -.0551267    .0213051
   exp_lowinc |   -.049674   .0200655    -2.48   0.014    -.0892364   -.0101115
  exp_migrant |  -.0475649   .0178141    -2.67   0.008    -.0826883   -.0124414
    exp_age50 |  -.0142615    .016297    -0.88   0.383    -.0463936    .0178706
     exp_male |  -.0388561   .0170043    -2.29   0.023    -.0723829   -.0053294
exp_co_ethnic |   .0381297   .0154309     2.47   0.014     .0077052    .0685542
   mal_border |  -.0390028   .0203426    -1.92   0.057    -.0791114    .0011059
        _cons |   .8396326   .0240584    34.90   0.000     .7921977    .8870675
-------------------------------------------------------------------------------

. *high ELF, Comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0 & elf > .39, cluster(sqkm)

Linear regression                               Number of obs     =      4,086
                                                F(8, 561)         =       4.23
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0082
                                                Root MSE          =     .43289

                                  (Std. Err. adjusted for 562 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0248746   .0129593    -1.92   0.055    -.0503293    .0005801
  exp_highinc |   -.012521     .01577    -0.79   0.428    -.0434965    .0184545
   exp_lowinc |  -.0402765   .0157775    -2.55   0.011    -.0712667   -.0092862
  exp_migrant |   -.047489   .0134265    -3.54   0.000    -.0738614   -.0211165
    exp_age50 |  -.0176656   .0143073    -1.23   0.217    -.0457681    .0104369
     exp_male |   .0068307   .0136957     0.50   0.618    -.0200704    .0337318
exp_co_ethnic |    .013274   .0129882     1.02   0.307    -.0122374    .0387854
   mal_border |  -.0420032   .0143028    -2.94   0.003    -.0700968   -.0139095
        _cons |    .820573   .0190961    42.97   0.000     .7830644    .8580815
-------------------------------------------------------------------------------

. 
. *(9) Sub-sample by patriarchy index
. *low patriarchy, individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le exp_co_ethnic mal_border ///
> if have_gov_title == 0 & prop_one_inf_woman < .1, cluster(sqkm)

Linear regression                               Number of obs     =      3,778
                                                F(8, 264)         =      11.78
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0275
                                                Root MSE          =      .4917

                                  (Std. Err. adjusted for 265 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0389086   .0158749    -2.45   0.015    -.0701661    -.007651
  exp_highinc |   .0140883   .0196972     0.72   0.475    -.0246954     .052872
   exp_lowinc |  -.0385459   .0227642    -1.69   0.092    -.0833684    .0062766
  exp_migrant |  -.0930686   .0165262    -5.63   0.000    -.1256084   -.0605287
    exp_age50 |  -.0040717   .0154521    -0.26   0.792    -.0344967    .0263533
     exp_male |  -.0159135   .0161967    -0.98   0.327    -.0478047    .0159778
exp_co_ethnic |   .0696509   .0158266     4.40   0.000     .0384884    .1008134
   mal_border |  -.1020735   .0199515    -5.12   0.000    -.1413579   -.0627891
        _cons |   .6499727   .0248674    26.14   0.000     .6010089    .6989364
-------------------------------------------------------------------------------

. *high patriarchy, individ. 
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le exp_co_ethnic mal_border  ///
> if have_gov_title == 0  & prop_one_inf_woman >= .1, cluster(sqkm)

Linear regression                               Number of obs     =      3,464
                                                F(8, 502)         =      13.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0314
                                                Root MSE          =     .49139

                                  (Std. Err. adjusted for 503 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0465945   .0174906    -2.66   0.008    -.0809583   -.0122307
  exp_highinc |   .0162794   .0182095     0.89   0.372    -.0194969    .0520558
   exp_lowinc |  -.0720666   .0207068    -3.48   0.001    -.1127493   -.0313839
  exp_migrant |  -.1093873   .0167549    -6.53   0.000    -.1423057   -.0764688
    exp_age50 |  -.0251717   .0185482    -1.36   0.175    -.0616133    .0112699
     exp_male |  -.0279661   .0161444    -1.73   0.084    -.0596849    .0037528
exp_co_ethnic |   .0350825   .0163917     2.14   0.033     .0028778    .0672872
   mal_border |  -.0951812   .0185462    -5.13   0.000    -.1316189   -.0587435
        _cons |   .6848843   .0242261    28.27   0.000     .6372872    .7324813
-------------------------------------------------------------------------------

. *low patriarchy, comm. 
.  reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age
> 50 exp_male exp_co_ethnic mal_border  ///
> if have_gov_title == 0 & prop_one_inf_woman < .1, cluster(sqkm)

Linear regression                               Number of obs     =      3,645
                                                F(8, 264)         =       4.73
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0101
                                                Root MSE          =     .43534

                                  (Std. Err. adjusted for 265 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0351053   .0136062    -2.58   0.010    -.0618957   -.0083149
  exp_highinc |  -.0220835   .0175817    -1.26   0.210    -.0567017    .0125348
   exp_lowinc |  -.0301974   .0165973    -1.82   0.070    -.0628774    .0024826
  exp_migrant |  -.0530705    .015909    -3.34   0.001    -.0843952   -.0217457
    exp_age50 |  -.0132201   .0133112    -0.99   0.322    -.0394297    .0129896
     exp_male |  -.0315623    .015043    -2.10   0.037    -.0611819   -.0019427
exp_co_ethnic |   .0307475    .014268     2.15   0.032      .002654     .058841
   mal_border |  -.0328861   .0160966    -2.04   0.042    -.0645802   -.0011921
        _cons |   .8288914   .0210643    39.35   0.000     .7874159    .8703668
-------------------------------------------------------------------------------

. *high patriarchy, comm. 
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male exp_co_ethnic mal_border  ///
> if have_gov_title == 0  & prop_one_inf_woman >= .1, cluster(sqkm)

Linear regression                               Number of obs     =      3,330
                                                F(8, 501)         =       4.74
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0112
                                                Root MSE          =     .43056

                                  (Std. Err. adjusted for 502 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0299592   .0154258    -1.94   0.053    -.0602664    .0003481
  exp_highinc |  -.0050013   .0172926    -0.29   0.773    -.0389763    .0289738
   exp_lowinc |  -.0596278   .0188207    -3.17   0.002    -.0966049   -.0226506
  exp_migrant |  -.0408775   .0142551    -2.87   0.004    -.0688846   -.0128705
    exp_age50 |  -.0189398   .0170709    -1.11   0.268    -.0524792    .0145996
     exp_male |   .0095151   .0151195     0.63   0.529    -.0201903    .0392204
exp_co_ethnic |   .0161257   .0138842     1.16   0.246    -.0111527    .0434041
   mal_border |  -.0490704   .0172361    -2.85   0.005    -.0829344   -.0152064
        _cons |    .826321   .0212412    38.90   0.000     .7845882    .8680539
-------------------------------------------------------------------------------

. 
. *(10) Sub-sample by patrilineal/matrilineal
. *not maj pat, individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le exp_co_ethnic mal_border ///
> if have_gov_title == 0 & majority_mat==1, cluster(sqkm)

Linear regression                               Number of obs     =      2,336
                                                F(8, 164)         =       5.52
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0212
                                                Root MSE          =      .4877

                                  (Std. Err. adjusted for 165 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0330762   .0212064    -1.56   0.121    -.0749489    .0087965
  exp_highinc |  -.0158096   .0246939    -0.64   0.523    -.0645685    .0329493
   exp_lowinc |  -.0997408   .0282847    -3.53   0.001    -.1555899   -.0438918
  exp_migrant |  -.0996642   .0204936    -4.86   0.000    -.1401295   -.0591989
    exp_age50 |  -.0035163   .0200654    -0.18   0.861    -.0431361    .0361034
     exp_male |   .0107088   .0184076     0.58   0.562    -.0256377    .0470553
exp_co_ethnic |    .041068   .0194991     2.11   0.037     .0025663    .0795698
   mal_border |  -.0022106   .0305673    -0.07   0.942    -.0625667    .0581456
        _cons |   .6709264    .026806    25.03   0.000     .6179971    .7238557
-------------------------------------------------------------------------------

. *maj pat, individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le exp_co_ethnic mal_border  ///
> if have_gov_title == 0  & majority_pat==1, cluster(sqkm)

Linear regression                               Number of obs     =      3,658
                                                F(8, 255)         =      16.29
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0346
                                                Root MSE          =     .49167

                                  (Std. Err. adjusted for 256 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0394194   .0160622    -2.45   0.015    -.0710508    -.007788
  exp_highinc |   .0320203   .0178514     1.79   0.074    -.0031346    .0671752
   exp_lowinc |  -.0320759    .021197    -1.51   0.131    -.0738194    .0096676
  exp_migrant |  -.1119921   .0165528    -6.77   0.000    -.1445898   -.0793944
    exp_age50 |  -.0123825   .0177961    -0.70   0.487    -.0474285    .0226635
     exp_male |  -.0508953   .0170692    -2.98   0.003    -.0845099   -.0172807
exp_co_ethnic |   .0844084   .0157003     5.38   0.000     .0534897    .1153271
   mal_border |  -.1169633   .0255621    -4.58   0.000    -.1673029   -.0666236
        _cons |   .6727042   .0313856    21.43   0.000     .6108961    .7345122
-------------------------------------------------------------------------------

. *not maj pat, comm.
. reg help_collect_donations  exp_title exp_highinc exp_lowinc exp_migrant exp_age
> 50 exp_male exp_co_ethnic mal_border ///
> if have_gov_title == 0 & majority_mat==1, cluster(sqkm)

Linear regression                               Number of obs     =      2,234
                                                F(8, 164)         =       3.24
                                                Prob > F          =     0.0019
                                                R-squared         =     0.0100
                                                Root MSE          =     .41435

                                  (Std. Err. adjusted for 165 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0085177   .0186086    -0.46   0.648    -.0452611    .0282257
  exp_highinc |  -.0336961   .0207638    -1.62   0.107     -.074695    .0073027
   exp_lowinc |  -.0625695   .0186952    -3.35   0.001    -.0994839   -.0256551
  exp_migrant |  -.0551287   .0187786    -2.94   0.004    -.0922076   -.0180498
    exp_age50 |  -.0257069   .0171413    -1.50   0.136     -.059553    .0081393
     exp_male |  -.0129943   .0168673    -0.77   0.442    -.0462994    .0203107
exp_co_ethnic |   .0118579   .0153629     0.77   0.441    -.0184766    .0421925
   mal_border |   .0117584   .0206316     0.57   0.570    -.0289793    .0524962
        _cons |   .8543218   .0217405    39.30   0.000     .8113944    .8972493
-------------------------------------------------------------------------------

. *maj pat, comm.
. reg help_collect_donations  exp_title exp_highinc exp_lowinc exp_migrant exp_age
> 50 exp_male exp_co_ethnic mal_border ///
> if have_gov_title == 0 & majority_pat==1, cluster(sqkm)

Linear regression                               Number of obs     =      3,542
                                                F(8, 255)         =       4.23
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0088
                                                Root MSE          =     .44411

                                  (Std. Err. adjusted for 256 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0315628   .0138132    -2.28   0.023    -.0587653   -.0043603
  exp_highinc |  -.0268527   .0173607    -1.55   0.123    -.0610414     .007336
   exp_lowinc |  -.0368679   .0178575    -2.06   0.040    -.0720348    -.001701
  exp_migrant |  -.0401755   .0153652    -2.61   0.009    -.0704344   -.0099166
    exp_age50 |  -.0149004   .0162925    -0.91   0.361    -.0469854    .0171846
     exp_male |  -.0307213   .0162002    -1.90   0.059    -.0626246     .001182
exp_co_ethnic |   .0437658   .0144197     3.04   0.003      .015369    .0721626
   mal_border |   -.031762   .0195461    -1.62   0.105    -.0702544    .0067304
        _cons |   .8099443   .0253227    31.98   0.000      .760076    .8598126
-------------------------------------------------------------------------------

. 
. *(11) Sub-sample by age
. *young, individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0 & older2 == 0, cluster(sqkm)

Linear regression                               Number of obs     =      3,779
                                                F(8, 693)         =      13.20
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0309
                                                Root MSE          =     .48882

                                  (Std. Err. adjusted for 694 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0279611   .0168023    -1.66   0.097    -.0609505    .0050284
  exp_highinc |   .0438115   .0182982     2.39   0.017     .0078849    .0797381
   exp_lowinc |  -.0450516   .0210443    -2.14   0.033    -.0863699   -.0037334
  exp_migrant |  -.1235693   .0163957    -7.54   0.000    -.1557605    -.091378
    exp_age50 |   -.019322   .0158255    -1.22   0.223    -.0503937    .0117496
     exp_male |  -.0206515   .0161071    -1.28   0.200    -.0522762    .0109731
exp_co_ethnic |    .054369   .0161035     3.38   0.001     .0227514    .0859865
   mal_border |  -.0782571   .0174049    -4.50   0.000    -.1124297   -.0440845
        _cons |   .6709003   .0235136    28.53   0.000     .6247338    .7170668
-------------------------------------------------------------------------------

. *old, individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0 & older2 == 1, cluster(sqkm)

Linear regression                               Number of obs     =      3,376
                                                F(8, 686)         =      11.80
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0286
                                                Root MSE          =     .49323

                                  (Std. Err. adjusted for 687 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0553015   .0166401    -3.32   0.001    -.0879731   -.0226299
  exp_highinc |  -.0233976   .0199543    -1.17   0.241    -.0625764    .0157813
   exp_lowinc |  -.0698142   .0217998    -3.20   0.001    -.1126166   -.0270119
  exp_migrant |  -.0738057   .0169787    -4.35   0.000    -.1071422   -.0404692
    exp_age50 |  -.0116047   .0176209    -0.66   0.510     -.046202    .0229927
     exp_male |  -.0267745   .0159577    -1.68   0.094    -.0581063    .0045572
exp_co_ethnic |   .0479861   .0167106     2.87   0.004     .0151762    .0807961
   mal_border |  -.1142397   .0188877    -6.05   0.000    -.1513244    -.077155
        _cons |   .6636348   .0244279    27.17   0.000     .6156723    .7115972
-------------------------------------------------------------------------------

. *young, comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0 & older2 == 0, cluster(sqkm)

Linear regression                               Number of obs     =      3,667
                                                F(8, 691)         =       2.01
                                                Prob > F          =     0.0423
                                                R-squared         =     0.0046
                                                Root MSE          =     .43084

                                  (Std. Err. adjusted for 692 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0105761   .0148758    -0.71   0.477    -.0397833    .0186312
  exp_highinc |   -.011164   .0163322    -0.68   0.494    -.0432307    .0209027
   exp_lowinc |   -.046321   .0176268    -2.63   0.009    -.0809295   -.0117125
  exp_migrant |  -.0356117   .0149161    -2.39   0.017    -.0648979   -.0063255
    exp_age50 |  -.0047971   .0140813    -0.34   0.733    -.0324444    .0228502
     exp_male |  -.0100476   .0142161    -0.71   0.480    -.0379596    .0178644
exp_co_ethnic |   .0144004   .0140038     1.03   0.304    -.0130946    .0418954
   mal_border |  -.0182851   .0156033    -1.17   0.242    -.0489206    .0123504
        _cons |   .8043337   .0213284    37.71   0.000     .7624574      .84621
-------------------------------------------------------------------------------

. *old, comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0 & older2 == 1, cluster(sqkm)

Linear regression                               Number of obs     =      3,226
                                                F(8, 680)         =       7.49
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0180
                                                Root MSE          =      .4348

                                  (Std. Err. adjusted for 681 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0528056   .0151222    -3.49   0.001    -.0824974   -.0231139
  exp_highinc |  -.0208859   .0183991    -1.14   0.257    -.0570117    .0152399
   exp_lowinc |  -.0422302   .0189102    -2.23   0.026    -.0793595   -.0051008
  exp_migrant |  -.0609019   .0153462    -3.97   0.000    -.0910336   -.0307703
    exp_age50 |  -.0220099   .0153477    -1.43   0.152    -.0521444    .0081247
     exp_male |  -.0196253   .0155009    -1.27   0.206    -.0500606    .0108101
exp_co_ethnic |   .0308337   .0157024     1.96   0.050     2.71e-06    .0616647
   mal_border |  -.0622363   .0161496    -3.85   0.000    -.0939454   -.0305272
        _cons |    .855032   .0212002    40.33   0.000     .8134063    .8966576
-------------------------------------------------------------------------------

. 
. *(12) Sub-sample by nativity
. *migrant, individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0 & native == 0, cluster(sqkm)

Linear regression                               Number of obs     =      4,591
                                                F(8, 719)         =      14.87
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0263
                                                Root MSE          =     .49255

                                  (Std. Err. adjusted for 720 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0348058   .0145179    -2.40   0.017    -.0633083   -.0063034
  exp_highinc |  -.0072447   .0169326    -0.43   0.669    -.0404879    .0259985
   exp_lowinc |  -.0561577   .0189948    -2.96   0.003    -.0934497   -.0188657
  exp_migrant |  -.0934963    .014731    -6.35   0.000    -.1224172   -.0645754
    exp_age50 |  -.0240308    .014865    -1.62   0.106    -.0532147    .0051532
     exp_male |   -.009891   .0144226    -0.69   0.493    -.0382064    .0184244
exp_co_ethnic |   .0643349   .0147048     4.38   0.000     .0354655    .0932044
   mal_border |  -.0983063   .0165771    -5.93   0.000    -.1308517   -.0657609
        _cons |   .6552751   .0212692    30.81   0.000      .613518    .6970322
-------------------------------------------------------------------------------

. *born in village, individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0 & native == 1, cluster(sqkm)

Linear regression                               Number of obs     =      2,643
                                                F(8, 605)         =      11.18
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0356
                                                Root MSE          =      .4895

                                  (Std. Err. adjusted for 606 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0555374    .019736    -2.81   0.005    -.0942968    -.016778
  exp_highinc |   .0515137   .0237018     2.17   0.030     .0049659    .0980615
   exp_lowinc |  -.0521259   .0248002    -2.10   0.036    -.1008308   -.0034209
  exp_migrant |  -.1153765   .0203065    -5.68   0.000    -.1552564   -.0754966
    exp_age50 |  -.0009325   .0200135    -0.05   0.963    -.0402368    .0383719
     exp_male |  -.0390071   .0185328    -2.10   0.036    -.0754036   -.0026106
exp_co_ethnic |   .0349737   .0190353     1.84   0.067    -.0024097     .072357
   mal_border |  -.0955597   .0208733    -4.58   0.000    -.1365527   -.0545668
        _cons |   .6851161   .0316233    21.66   0.000     .6230112    .7472209
-------------------------------------------------------------------------------

. *migrant, comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0 & native == 0, cluster(sqkm)

Linear regression                               Number of obs     =      4,422
                                                F(8, 717)         =       6.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0112
                                                Root MSE          =     .43199

                                  (Std. Err. adjusted for 718 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0282249   .0127463    -2.21   0.027    -.0532493   -.0032004
  exp_highinc |  -.0313417   .0159468    -1.97   0.050    -.0626496   -.0000338
   exp_lowinc |  -.0524853   .0150379    -3.49   0.001    -.0820089   -.0229618
  exp_migrant |  -.0443208   .0130785    -3.39   0.001    -.0699974   -.0186441
    exp_age50 |  -.0165819   .0129759    -1.28   0.202    -.0420571    .0088933
     exp_male |  -.0036679   .0127029    -0.29   0.773    -.0286073    .0212715
exp_co_ethnic |   .0247085   .0122396     2.02   0.044     .0006787    .0487383
   mal_border |  -.0566109   .0136333    -4.15   0.000    -.0833769   -.0298449
        _cons |   .8384047   .0180481    46.45   0.000     .8029713    .8738381
-------------------------------------------------------------------------------

. *born in village, comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0 & native == 1, cluster(sqkm)

Linear regression                               Number of obs     =      2,547
                                                F(8, 601)         =       2.40
                                                Prob > F          =     0.0150
                                                R-squared         =     0.0094
                                                Root MSE          =     .43512

                                  (Std. Err. adjusted for 602 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0391345   .0184653    -2.12   0.034    -.0753988   -.0028702
  exp_highinc |    .012759   .0211454     0.60   0.546    -.0287689     .054287
   exp_lowinc |  -.0315147   .0215052    -1.47   0.143    -.0737492    .0107198
  exp_migrant |  -.0526888   .0190883    -2.76   0.006    -.0901766   -.0152009
    exp_age50 |   -.016302   .0181653    -0.90   0.370    -.0519772    .0193732
     exp_male |  -.0265838   .0170019    -1.56   0.118    -.0599741    .0068065
exp_co_ethnic |    .022017   .0170897     1.29   0.198    -.0115457    .0555798
   mal_border |   -.013067    .019472    -0.67   0.502    -.0513084    .0251744
        _cons |   .8129211     .02711    29.99   0.000     .7596794    .8661629
-------------------------------------------------------------------------------

. 
. *(13) Sub-sample by by inheritance of land
. *not fam inherit, indivd.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0 & Acquiredfamily == 0, cluster(s
> qkm)

Linear regression                               Number of obs     =      2,576
                                                F(8, 618)         =      10.36
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0326
                                                Root MSE          =      .4898

                                  (Std. Err. adjusted for 619 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |   -.020591   .0180371    -1.14   0.254    -.0560125    .0148305
  exp_highinc |   .0036046   .0232697     0.15   0.877    -.0420926    .0493018
   exp_lowinc |  -.0792809   .0244039    -3.25   0.001    -.1272055   -.0313563
  exp_migrant |  -.0871088     .01973    -4.42   0.000    -.1258548   -.0483627
    exp_age50 |  -.0235879   .0195796    -1.20   0.229    -.0620385    .0148626
     exp_male |  -.0107559   .0199739    -0.54   0.590    -.0499809    .0284691
exp_co_ethnic |   .0481874   .0189935     2.54   0.011     .0108878    .0854869
   mal_border |  -.1280511   .0203883    -6.28   0.000    -.1680898   -.0880124
        _cons |   .6839227   .0284898    24.01   0.000     .6279742    .7398711
-------------------------------------------------------------------------------

. *fam inherit, individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0 & Acquiredfamily == 1, cluster(s
> qkm)

Linear regression                               Number of obs     =      4,581
                                                F(8, 684)         =      14.87
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0281
                                                Root MSE          =     .49231

                                  (Std. Err. adjusted for 685 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0548421   .0156674    -3.50   0.000    -.0856041   -.0240802
  exp_highinc |   .0174854   .0174542     1.00   0.317     -.016785    .0517557
   exp_lowinc |  -.0460234   .0197423    -2.33   0.020    -.0847861   -.0072606
  exp_migrant |  -.1092844   .0150049    -7.28   0.000    -.1387455   -.0798233
    exp_age50 |  -.0087163   .0148103    -0.59   0.556    -.0377954    .0203628
     exp_male |  -.0307492   .0142415    -2.16   0.031    -.0587116   -.0027869
exp_co_ethnic |   .0583106   .0146104     3.99   0.000      .029624    .0869973
   mal_border |  -.0790453    .017346    -4.56   0.000    -.1131031   -.0449875
        _cons |   .6564684   .0233855    28.07   0.000     .6105523    .7023844
-------------------------------------------------------------------------------

. *not fam inherit, comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0 & Acquiredfamily == 0, cluster(s
> qkm)

Linear regression                               Number of obs     =      2,475
                                                F(8, 611)         =       2.99
                                                Prob > F          =     0.0027
                                                R-squared         =     0.0093
                                                Root MSE          =     .43572

                                  (Std. Err. adjusted for 612 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0102914   .0178712    -0.58   0.565    -.0453878     .024805
  exp_highinc |  -.0297019    .020394    -1.46   0.146    -.0697527    .0103488
   exp_lowinc |   -.057678   .0203316    -2.84   0.005    -.0976064   -.0177496
  exp_migrant |   -.044104   .0175186    -2.52   0.012     -.078508   -.0097001
    exp_age50 |  -.0365563   .0174812    -2.09   0.037    -.0708867   -.0022258
     exp_male |  -.0146282   .0177425    -0.82   0.410    -.0494719    .0202156
exp_co_ethnic |   .0071149   .0170884     0.42   0.677    -.0264442     .040674
   mal_border |  -.0362837   .0180164    -2.01   0.044    -.0716652   -.0009022
        _cons |   .8379715   .0257004    32.61   0.000     .7874998    .8884433
-------------------------------------------------------------------------------

. *fam inherit, comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0 & Acquiredfamily == 1, cluster(s
> qkm)

Linear regression                               Number of obs     =      4,419
                                                F(8, 682)         =       5.52
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0114
                                                Root MSE          =     .43089

                                  (Std. Err. adjusted for 683 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0450989   .0133235    -3.38   0.001     -.071259   -.0189389
  exp_highinc |  -.0031489   .0155795    -0.20   0.840    -.0337385    .0274406
   exp_lowinc |  -.0378431    .016505    -2.29   0.022    -.0702498   -.0054363
  exp_migrant |  -.0468944   .0136179    -3.44   0.001    -.0736325   -.0201562
    exp_age50 |  -.0038099   .0137179    -0.28   0.781    -.0307442    .0231244
     exp_male |  -.0094617   .0138151    -0.68   0.494     -.036587    .0176636
exp_co_ethnic |   .0343075   .0128386     2.67   0.008     .0090995    .0595154
   mal_border |  -.0447302   .0147088    -3.04   0.002    -.0736102   -.0158501
        _cons |   .8224676   .0199157    41.30   0.000     .7833641     .861571
-------------------------------------------------------------------------------

. 
. *(14) Sub-sample by high/low rates of titling
. *low titling, individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0 & prop_titlers <= .06, cluster(s
> qkm)

Linear regression                               Number of obs     =      4,434
                                                F(8, 286)         =      15.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0287
                                                Root MSE          =      .4902

                                  (Std. Err. adjusted for 287 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |   -.046702   .0151041    -3.09   0.002    -.0764313   -.0169726
  exp_highinc |   .0235148   .0171433     1.37   0.171    -.0102282    .0572577
   exp_lowinc |  -.0545112   .0195865    -2.78   0.006    -.0930632   -.0159592
  exp_migrant |  -.0977501   .0151132    -6.47   0.000    -.1274973    -.068003
    exp_age50 |  -.0169208   .0146725    -1.15   0.250    -.0458006    .0119589
     exp_male |  -.0223444   .0145321    -1.54   0.125    -.0509479    .0062591
exp_co_ethnic |   .0523065   .0143764     3.64   0.000     .0240096    .0806034
   mal_border |  -.0972037   .0170455    -5.70   0.000    -.1307543    -.063653
        _cons |   .6735687   .0209084    32.22   0.000     .6324149    .7147224
-------------------------------------------------------------------------------

. *high titling, individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 0 & prop_titlers > .06, cluster(sq
> km)

Linear regression                               Number of obs     =      2,808
                                                F(8, 480)         =       8.11
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0257
                                                Root MSE          =     .49402

                                  (Std. Err. adjusted for 481 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |   -.035682   .0189264    -1.89   0.060    -.0728709    .0015068
  exp_highinc |   .0010742   .0216967     0.05   0.961     -.041558    .0437065
   exp_lowinc |  -.0561397   .0255411    -2.20   0.028    -.1063258   -.0059536
  exp_migrant |  -.1051395   .0189025    -5.56   0.000    -.1422813   -.0679977
    exp_age50 |   -.010513    .020511    -0.51   0.608    -.0508154    .0297894
     exp_male |  -.0209257   .0185658    -1.13   0.260     -.057406    .0155545
exp_co_ethnic |   .0535809   .0186892     2.87   0.004     .0168583    .0903036
   mal_border |  -.0882023   .0241539    -3.65   0.000    -.1356627   -.0407419
        _cons |   .6489604   .0310768    20.88   0.000     .5878969    .7100238
-------------------------------------------------------------------------------

. *low titling, comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0 & prop_titlers <= .06, cluster(s
> qkm)

Linear regression                               Number of obs     =      4,260
                                                F(8, 286)         =       6.32
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0113
                                                Root MSE          =     .42967

                                  (Std. Err. adjusted for 287 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0296945   .0134576    -2.21   0.028    -.0561831    -.003206
  exp_highinc |  -.0203482   .0158644    -1.28   0.201     -.051574    .0108777
   exp_lowinc |  -.0524448   .0155638    -3.37   0.001    -.0830788   -.0218108
  exp_migrant |  -.0511377   .0138185    -3.70   0.000    -.0783366   -.0239389
    exp_age50 |  -.0184872   .0132351    -1.40   0.164    -.0445379    .0075634
     exp_male |  -.0169986   .0138717    -1.23   0.221    -.0443021    .0103049
exp_co_ethnic |   .0409336   .0128058     3.20   0.002     .0157279    .0661392
   mal_border |  -.0295733   .0154296    -1.92   0.056    -.0599433    .0007967
        _cons |   .8266515   .0179187    46.13   0.000     .7913822    .8619209
-------------------------------------------------------------------------------

. *high titling, comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0 & prop_titlers > .06, cluster(sq
> km)

Linear regression                               Number of obs     =      2,715
                                                F(8, 479)         =       2.79
                                                Prob > F          =     0.0050
                                                R-squared         =     0.0090
                                                Root MSE          =      .4384

                                  (Std. Err. adjusted for 480 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |  -.0370252   .0156791    -2.36   0.019    -.0678335   -.0062168
  exp_highinc |   -.003545    .019273    -0.18   0.854    -.0414151    .0343251
   exp_lowinc |  -.0300841   .0206993    -1.45   0.147    -.0707567    .0105885
  exp_migrant |  -.0406644   .0170471    -2.39   0.017    -.0741606   -.0071681
    exp_age50 |  -.0127199   .0182579    -0.70   0.486    -.0485953    .0231555
     exp_male |  -.0042593   .0169754    -0.25   0.802    -.0376147    .0290961
exp_co_ethnic |  -.0051769    .015805    -0.33   0.743    -.0362325    .0258788
   mal_border |  -.0562155   .0193469    -2.91   0.004    -.0942308   -.0182002
        _cons |   .8325852   .0266257    31.27   0.000     .7802675    .8849029
-------------------------------------------------------------------------------

. 
. 
. *Table C.6
. *Title x high income interaction; individ. ... some rounding issues in original 
> table
. reg trust_pay_back exp_title##exp_highinc exp_lowinc exp_migrant exp_age50 exp_m
> ale exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,242
                                                F(9, 767)         =      21.33
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0292
                                                Root MSE          =     .49133

                                  (Std. Err. adjusted for 768 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
  1.exp_title |  -.0637045   .0146178    -4.36   0.000    -.0924001   -.0350088
1.exp_highinc |  -.0166434   .0181627    -0.92   0.360    -.0522978    .0190111
              |
    exp_title#|
  exp_highinc |
         1 1  |   .0633883    .024812     2.55   0.011     .0146809    .1120957
              |
   exp_lowinc |   -.054876   .0155425    -3.53   0.000    -.0853869   -.0243651
  exp_migrant |  -.1008787   .0117741    -8.57   0.000     -.123992   -.0777654
    exp_age50 |  -.0137286   .0119877    -1.15   0.252    -.0372611     .009804
     exp_male |  -.0216768   .0114792    -1.89   0.059    -.0442111    .0008575
exp_co_ethnic |   .0533404   .0114006     4.68   0.000     .0309603    .0757206
   mal_border |  -.0964295   .0137077    -7.03   0.000    -.1233386   -.0695204
        _cons |   .6756487    .017896    37.75   0.000     .6405178    .7107797
-------------------------------------------------------------------------------

. *Title x high income interaction; comm. ... some rounding issues, missing single
>  star on interaction term in original table and typo on sample size
. reg help_collect_donations exp_title##exp_highinc exp_lowinc exp_migrant exp_age
> 50 exp_male exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,975
                                                F(9, 766)         =       7.28
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0098
                                                Root MSE          =     .43301

                                  (Std. Err. adjusted for 767 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
  1.exp_title |  -.0447645   .0133354    -3.36   0.001    -.0709428   -.0185862
1.exp_highinc |   -.032066   .0156591    -2.05   0.041    -.0628059   -.0013261
              |
    exp_title#|
  exp_highinc |
         1 1  |    .036332   .0218848     1.66   0.097    -.0066292    .0792932
              |
   exp_lowinc |  -.0444429   .0124818    -3.56   0.000    -.0689455   -.0199403
  exp_migrant |  -.0469315   .0107513    -4.37   0.000     -.068037    -.025826
    exp_age50 |  -.0150032   .0107517    -1.40   0.163    -.0361094    .0061031
     exp_male |  -.0120647   .0107526    -1.12   0.262    -.0331728    .0090433
exp_co_ethnic |    .023485    .009975     2.35   0.019     .0039034    .0430667
   mal_border |  -.0406741   .0118495    -3.43   0.001    -.0639354   -.0174128
        _cons |    .833983   .0154804    53.87   0.000     .8035941     .864372
-------------------------------------------------------------------------------

. 
. *Title x migrant interaction; individ.
. reg trust_pay_back exp_title##exp_migrant exp_highinc exp_lowinc exp_age50 exp_m
> ale exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,242
                                                F(9, 767)         =      20.91
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0284
                                                Root MSE          =     .49153

                                  (Std. Err. adjusted for 768 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
  1.exp_title |  -.0542779   .0173839    -3.12   0.002    -.0884035   -.0201523
1.exp_migrant |  -.1129115   .0167868    -6.73   0.000    -.1458651   -.0799579
              |
    exp_title#|
  exp_migrant |
         1 1  |   .0237228   .0236267     1.00   0.316    -.0226578    .0701035
              |
  exp_highinc |   .0151241   .0134605     1.12   0.262    -.0112998     .041548
   exp_lowinc |  -.0544704   .0155543    -3.50   0.000    -.0850044   -.0239363
    exp_age50 |  -.0145122   .0120051    -1.21   0.227     -.038079    .0090546
     exp_male |  -.0216807   .0114665    -1.89   0.059    -.0441902    .0008288
exp_co_ethnic |   .0531607   .0114124     4.66   0.000     .0307574     .075564
   mal_border |  -.0968387   .0137169    -7.06   0.000    -.1237658   -.0699117
        _cons |   .6713329    .017888    37.53   0.000     .6362177    .7064482
-------------------------------------------------------------------------------

. *Title x migrant interaction; comm.
. reg help_collect_donations exp_title##exp_migrant exp_highinc exp_lowinc exp_age
> 50 exp_male exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,975
                                                F(9, 766)         =       7.11
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0094
                                                Root MSE          =     .43309

                                  (Std. Err. adjusted for 767 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
  1.exp_title |   -.031525    .014516    -2.17   0.030    -.0600208   -.0030292
1.exp_migrant |  -.0461557    .014591    -3.16   0.002    -.0747988   -.0175126
              |
    exp_title#|
  exp_migrant |
         1 1  |  -.0018086   .0210181    -0.09   0.931    -.0430684    .0394513
              |
  exp_highinc |  -.0139847   .0122642    -1.14   0.255    -.0380601    .0100906
   exp_lowinc |   -.044344   .0124916    -3.55   0.000    -.0688659   -.0198222
    exp_age50 |  -.0153554   .0107578    -1.43   0.154    -.0364738    .0057629
     exp_male |  -.0119974    .010729    -1.12   0.264     -.033059    .0090643
exp_co_ethnic |   .0233052    .009971     2.34   0.020     .0037314     .042879
   mal_border |  -.0409122   .0118702    -3.45   0.001    -.0642142   -.0176102
        _cons |   .8277687   .0152515    54.27   0.000     .7978291    .8577084
-------------------------------------------------------------------------------

. 
. *Title x 50 yo interaction; individ.
. reg trust_pay_back exp_title##exp_age50 exp_highinc exp_lowinc exp_migrant  exp_
> male exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,242
                                                F(9, 767)         =      20.96
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0286
                                                Root MSE          =     .49148

                                  (Std. Err. adjusted for 768 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
  1.exp_title |  -.0600251   .0158925    -3.78   0.000     -.091223   -.0288273
  1.exp_age50 |  -.0322882   .0166175    -1.94   0.052    -.0649093    .0003329
              |
    exp_title#|
    exp_age50 |
         1 1  |   .0360792   .0227686     1.58   0.113     -.008617    .0807755
              |
  exp_highinc |   .0151075   .0134558     1.12   0.262    -.0113071    .0415221
   exp_lowinc |  -.0551117   .0155145    -3.55   0.000    -.0855676   -.0246558
  exp_migrant |  -.1013558    .011791    -8.60   0.000    -.1245023   -.0782094
     exp_male |   -.021643   .0114452    -1.89   0.059    -.0441107    .0008247
exp_co_ethnic |   .0531399   .0114229     4.65   0.000      .030716    .0755638
   mal_border |  -.0966769   .0137184    -7.05   0.000     -.123607   -.0697469
        _cons |   .6745466   .0182956    36.87   0.000     .6386312    .7104619
-------------------------------------------------------------------------------

. *Title x 50 yo interaction; comm.
. reg help_collect_donations exp_title##exp_age50 exp_highinc exp_lowinc exp_migra
> nt  exp_male exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,975
                                                F(9, 766)         =       7.91
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0103
                                                Root MSE          =     .43289

                                  (Std. Err. adjusted for 767 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
  1.exp_title |  -.0583618      .0142    -4.11   0.000    -.0862374   -.0304862
  1.exp_age50 |  -.0415105   .0146631    -2.83   0.005    -.0702951   -.0127258
              |
    exp_title#|
    exp_age50 |
         1 1  |   .0525646   .0202465     2.60   0.010     .0128195    .0923097
              |
  exp_highinc |  -.0136417   .0122472    -1.11   0.266    -.0376838    .0104004
   exp_lowinc |  -.0448588    .012478    -3.60   0.000    -.0693539   -.0203638
  exp_migrant |   -.047444   .0107623    -4.41   0.000    -.0685712   -.0263169
     exp_male |  -.0120172   .0107184    -1.12   0.263     -.033058    .0090237
exp_co_ethnic |   .0234094   .0099704     2.35   0.019     .0038369    .0429819
   mal_border |  -.0405895   .0118673    -3.42   0.001    -.0638858   -.0172931
        _cons |   .8411371    .015729    53.48   0.000       .81026    .8720142
-------------------------------------------------------------------------------

. 
. *Title x male interaction; individ.
. reg trust_pay_back exp_title##exp_male exp_highinc exp_lowinc exp_migrant exp_ag
> e50 exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,242
                                                F(9, 767)         =      20.88
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0283
                                                Root MSE          =     .49156

                                  (Std. Err. adjusted for 768 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
  1.exp_title |  -.0421802   .0164595    -2.56   0.011    -.0744912   -.0098693
   1.exp_male |  -.0215198   .0155879    -1.38   0.168    -.0521198    .0090802
              |
    exp_title#|
     exp_male |
         1 1  |  -.0001698   .0223618    -0.01   0.994    -.0440674    .0437278
              |
  exp_highinc |   .0149002   .0134607     1.11   0.269     -.011524    .0413243
   exp_lowinc |  -.0547132   .0155345    -3.52   0.000    -.0852084    -.024218
  exp_migrant |  -.1011331   .0117802    -8.59   0.000    -.1242583   -.0780079
    exp_age50 |  -.0143642   .0120161    -1.20   0.232    -.0379526    .0092242
exp_co_ethnic |   .0531205   .0113984     4.66   0.000     .0307447    .0754962
   mal_border |  -.0968548   .0137226    -7.06   0.000    -.1237931   -.0699165
        _cons |   .6655939   .0181163    36.74   0.000     .6300304    .7011573
-------------------------------------------------------------------------------

. *Title x male interaction; comm.
. reg help_collect_donations exp_title##exp_male exp_highinc exp_lowinc exp_migran
> t exp_age50 exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,975
                                                F(9, 766)         =       7.09
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0095
                                                Root MSE          =     .43309

                                  (Std. Err. adjusted for 767 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
  1.exp_title |  -.0275311   .0144818    -1.90   0.058    -.0559598    .0008976
   1.exp_male |   -.007019   .0144559    -0.49   0.627    -.0353968    .0213588
              |
    exp_title#|
     exp_male |
         1 1  |  -.0100377    .021588    -0.46   0.642    -.0524164    .0323409
              |
  exp_highinc |  -.0139784   .0122552    -1.14   0.254    -.0380363    .0100794
   exp_lowinc |   -.044377   .0124915    -3.55   0.000    -.0688988   -.0198553
  exp_migrant |  -.0469973   .0107543    -4.37   0.000    -.0681087   -.0258859
    exp_age50 |  -.0153683   .0107534    -1.43   0.153    -.0364779    .0057412
exp_co_ethnic |   .0232514   .0099734     2.33   0.020      .003673    .0428299
   mal_border |   -.041008    .011879    -3.45   0.001    -.0643274   -.0176887
        _cons |   .8257905   .0153499    53.80   0.000     .7956575    .8559234
-------------------------------------------------------------------------------

. 
. *Title x co-ethnic interaction; individ.
. reg trust_pay_back exp_title##exp_co_ethnic exp_highinc exp_lowinc exp_migrant e
> xp_age50 exp_male mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      7,242
                                                F(9, 767)         =      20.80
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0283
                                                Root MSE          =     .49156

                                    (Std. Err. adjusted for 768 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
 trust_pay_back |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
    1.exp_title |  -.0385999   .0165199    -2.34   0.020    -.0710294   -.0061703
1.exp_co_ethnic |   .0567498   .0156009     3.64   0.000     .0261243    .0873753
                |
      exp_title#|
  exp_co_ethnic |
           1 1  |  -.0073048   .0233077    -0.31   0.754    -.0530593    .0384498
                |
    exp_highinc |   .0148745   .0134673     1.10   0.270    -.0115627    .0413117
     exp_lowinc |  -.0547064   .0155324    -3.52   0.000    -.0851975   -.0242153
    exp_migrant |  -.1011486   .0117816    -8.59   0.000    -.1242766   -.0780207
      exp_age50 |  -.0143667   .0120143    -1.20   0.232    -.0379516    .0092182
       exp_male |  -.0216446   .0114605    -1.89   0.059    -.0441424    .0008531
     mal_border |  -.0968847     .01371    -7.07   0.000    -.1237983    -.069971
          _cons |   .6638788   .0182842    36.31   0.000     .6279858    .6997719
---------------------------------------------------------------------------------

. *Title x co-ethnic interaction; comm.
. reg help_collect_donations exp_title##exp_co_ethnic exp_highinc exp_lowinc exp_m
> igrant exp_age50 exp_male mal_border if have_gov_title == 0, cluster(sqkm)

Linear regression                               Number of obs     =      6,975
                                                F(9, 766)         =       7.10
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0095
                                                Root MSE          =     .43307

                                    (Std. Err. adjusted for 767 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
help_collect_~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
    1.exp_title |  -.0413625   .0148517    -2.79   0.005    -.0705174   -.0122076
1.exp_co_ethnic |   .0145043   .0136193     1.06   0.287    -.0122312    .0412398
                |
      exp_title#|
  exp_co_ethnic |
           1 1  |   .0177089   .0205299     0.86   0.389    -.0225925    .0580104
                |
    exp_highinc |  -.0138776   .0122611    -1.13   0.258     -.037947    .0101919
     exp_lowinc |  -.0443359   .0124936    -3.55   0.000    -.0688617     -.01981
    exp_migrant |      -.047   .0107592    -4.37   0.000    -.0681211   -.0258789
      exp_age50 |  -.0153386   .0107511    -1.43   0.154    -.0364438    .0057666
       exp_male |  -.0119045   .0107347    -1.11   0.268    -.0329775    .0091685
     mal_border |  -.0408391    .011869    -3.44   0.001    -.0641387   -.0175394
          _cons |   .8324662   .0157837    52.74   0.000     .8014817    .8634507
---------------------------------------------------------------------------------

. 
. 
. *** Appendix D ***
. 
. *Table D1 
. *Malawi; minor rounding error for government title
. sum have_gov_title single_male single_female  resp_highinc resp_lowinc primary_s
> chooling  ///
>  secondaryplus age  land_size aglanduse Acquiredfamily resp_migrant10 ///
>  native resp_maj_eth contributed local_obligation_to_help if mal_border == 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
have_gov_t~e |      4,069    .1076432    .3099673          0          1
 single_male |      6,797    .0957775    .2943076          0          1
single_fem~e |      6,797    .1912609    .3933229          0          1
resp_highinc |      6,726    .1525424    .3595726          0          1
 resp_lowinc |      6,726     .436515    .4959902          0          1
-------------+---------------------------------------------------------
primary_sc~g |      6,757    .5850229    .4927545          0          1
secondaryp~s |      6,757    .3218884     .467235          0          1
         age |      6,759    36.06451    15.47707         18         99
   land_size |      4,344    2.159716    28.94512   .0202347       1800
   aglanduse |      4,245    .8845701    .3195777          0          1
-------------+---------------------------------------------------------
Acquiredfa~y |      4,265    .6579132    .4744641          0          1
resp_migr~10 |      6,791    .3691651    .4826143          0          1
      native |      6,791    .3027536    .4594834          0          1
resp_maj_eth |      6,678    .6365678    .4810238          0          1
 contributed |      6,637     .077746    .2677916          0          1
-------------+---------------------------------------------------------
local_obli~p |      6,696    .3316906    .4708556          0          1

. 
. *Table D2 
. *Zambia; minor roudning error for conribued in the past year
. sum have_gov_title single_male single_female  resp_highinc resp_lowinc primary_s
> chooling  ///
>  secondaryplus age  land_size aglanduse Acquiredfamily resp_migrant10 ///
>  native resp_maj_eth contributed local_obligation_to_help if mal_border == 0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
have_gov_t~e |      3,902    .0725269    .2593916          0          1
 single_male |      5,563    .1114507    .3147177          0          1
single_fem~e |      5,563    .2255977    .4180129          0          1
resp_highinc |      5,532    .2160159    .4115625          0          1
 resp_lowinc |      5,532    .3461678    .4757905          0          1
-------------+---------------------------------------------------------
primary_sc~g |      5,553    .5530344    .4972242          0          1
secondaryp~s |      5,553    .3092022    .4622063          0          1
         age |      5,489     37.1206    15.99328         18         97
   land_size |      3,784    6.497022    40.98889   1.00e-05    1200.12
   aglanduse |      3,857    .8325123    .3734591          0          1
-------------+---------------------------------------------------------
Acquiredfa~y |      3,997    .6004503    .4898671          0          1
resp_migr~10 |      5,554    .3501981    .4770748          0          1
      native |      5,554    .3082463    .4618105          0          1
resp_maj_eth |      5,521    .6386524    .4804345          0          1
 contributed |      5,390    .1486085    .3557352          0          1
-------------+---------------------------------------------------------
local_obli~p |      5,477    .4409348    .4965444          0          1

.  
. *Table D3
. *pooled, with title; some minor rounding errors and typos in original table for 
> migrant and land size
. sum  single_male single_female  resp_highinc resp_lowinc primary_schooling  ///
>  secondaryplus age  land_size aglanduse Acquiredfamily resp_migrant10 ///
>  native resp_maj_eth contributed local_obligation_to_help if  have_gov_title==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 single_male |        721    .1373093    .3444125          0          1
single_fem~e |        721    .2177531    .4130052          0          1
resp_highinc |        715    .2937063    .4557778          0          1
 resp_lowinc |        715    .2825175    .4505388          0          1
primary_sc~g |        720    .4416667    .4969308          0          1
-------------+---------------------------------------------------------
secondaryp~s |        720    .4722222    .4995748          0          1
         age |        719    38.87344    16.82788         18         88
   land_size |        700    8.235808    76.45864     .00003       1800
   aglanduse |        697     .713056    .4526601          0          1
Acquiredfa~y |        693    .5122655    .5002106          0          1
-------------+---------------------------------------------------------
resp_migr~10 |        721    .3079057    .4619478          0          1
      native |        721      .29681    .4571692          0          1
resp_maj_eth |        709    .5105783     .500241          0          1
 contributed |        708    .1129944    .3168097          0          1
local_obli~p |        711    .3924051    .4886299          0          1

. 
. *Table D4
. *pooled, without title; minor rounding error for ag. land
. sum  single_male single_female  resp_highinc resp_lowinc primary_schooling  ///
>  secondaryplus age  land_size aglanduse Acquiredfamily resp_migrant10 ///
>  native resp_maj_eth contributed local_obligation_to_help if have_gov_title==0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 single_male |      7,249    .0902193    .2865155          0          1
single_fem~e |      7,249    .2000276    .4000483          0          1
resp_highinc |      7,229    .1647531    .3709832          0          1
 resp_lowinc |      7,229    .4086319     .491615          0          1
primary_sc~g |      7,245    .6042788    .4890388          0          1
-------------+---------------------------------------------------------
secondaryp~s |      7,245    .2706694    .4443363          0          1
         age |      7,163    38.40053    16.17023         18         99
   land_size |      7,003    3.770864    28.36495   1.00e-05    1200.12
   aglanduse |      6,951    .8762768    .3292892          0          1
Acquiredfa~y |      7,165    .6399163    .4800579          0          1
-------------+---------------------------------------------------------
resp_migr~10 |      7,242    .2680199    .4429586          0          1
      native |      7,242    .3652306     .481528          0          1
resp_maj_eth |      7,150    .6896503    .4626691          0          1
 contributed |      7,101     .127306    .3333389          0          1
local_obli~p |      7,182    .4061543    .4911482          0          1

. 
. *Figure D1
. 
. *Malawi D1(a)
. logit have_gov_title single_male single_female  resp_highinc resp_lowinc primary
> _schooling  ///
>  secondaryplus age  land_size aglanduse Acquiredfamily resp_migrant10 ///
>  native resp_maj_eth contributed local_obligation_to_help if mal_border == 1 , c
> luster(sqkm)

Iteration 0:   log pseudolikelihood = -1237.0714  
Iteration 1:   log pseudolikelihood = -1184.0697  
Iteration 2:   log pseudolikelihood =  -1148.288  
Iteration 3:   log pseudolikelihood = -1146.2231  
Iteration 4:   log pseudolikelihood = -1146.2145  
Iteration 5:   log pseudolikelihood = -1146.2145  

Logistic regression                             Number of obs     =      3,638
                                                Wald chi2(15)     =      76.77
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1146.2145               Pseudo R2         =     0.0734

                                    (Std. Err. adjusted for 352 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
 have_gov_title |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
    single_male |    .172215   .2023198     0.85   0.395    -.2243245    .5687545
  single_female |   .1095618   .1528019     0.72   0.473    -.1899245    .4090481
   resp_highinc |   .1555358   .1412086     1.10   0.271     -.121228    .4322995
    resp_lowinc |  -.2601011   .1330119    -1.96   0.051    -.5207997    .0005975
primary_schoo~g |   .1491654   .2241083     0.67   0.506    -.2900789    .5884097
  secondaryplus |   .5873745   .2362208     2.49   0.013     .1243903    1.050359
            age |   .0069393   .0039102     1.77   0.076    -.0007245    .0146031
      land_size |   .1186914   .0267268     4.44   0.000     .0663077     .171075
      aglanduse |  -.6275938   .1875702    -3.35   0.001    -.9952246   -.2599631
 Acquiredfamily |  -.3114416   .1239693    -2.51   0.012     -.554417   -.0684662
 resp_migrant10 |  -.3139553   .1791749    -1.75   0.080    -.6651317     .037221
         native |  -.0326014   .1527269    -0.21   0.831    -.3319406    .2667378
   resp_maj_eth |  -.1575674   .1551933    -1.02   0.310    -.4617408    .1466059
    contributed |   .0368941   .1959057     0.19   0.851     -.347074    .4208622
local_obligat~p |   .0808015   .1350481     0.60   0.550    -.1838878    .3454908
          _cons |  -1.985428   .3846566    -5.16   0.000    -2.739341   -1.231515
---------------------------------------------------------------------------------
Note: 0 failures and 1 success completely determined.

. est sto Malawi

. 
. logit have_gov_title single_male single_female  resp_highinc resp_lowinc primary
> _schooling  ///
>  secondaryplus age  land_size aglanduse Acquiredfamily resp_migrant10 ///
>  native resp_maj_eth if mal_border == 1 , cluster(sqkm)

Iteration 0:   log pseudolikelihood = -1258.2782  
Iteration 1:   log pseudolikelihood = -1205.8655  
Iteration 2:   log pseudolikelihood = -1169.9853  
Iteration 3:   log pseudolikelihood = -1167.9235  
Iteration 4:   log pseudolikelihood = -1167.9148  
Iteration 5:   log pseudolikelihood = -1167.9148  

Logistic regression                             Number of obs     =      3,732
                                                Wald chi2(13)     =      73.11
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1167.9148               Pseudo R2         =     0.0718

                                    (Std. Err. adjusted for 353 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
 have_gov_title |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
    single_male |   .1635531   .1990316     0.82   0.411    -.2265416    .5536478
  single_female |   .0997951     .15294     0.65   0.514    -.1999617    .3995518
   resp_highinc |   .1967129   .1441267     1.36   0.172    -.0857702     .479196
    resp_lowinc |  -.2525171   .1326338    -1.90   0.057    -.5124747    .0074404
primary_schoo~g |   .1951107   .2234947     0.87   0.383    -.2429309    .6331523
  secondaryplus |    .616308   .2367789     2.60   0.009     .1522298    1.080386
            age |   .0066654   .0038608     1.73   0.084    -.0009016    .0142325
      land_size |   .1182573   .0263745     4.48   0.000     .0665642    .1699504
      aglanduse |  -.6453755   .1873093    -3.45   0.001    -1.012495   -.2782561
 Acquiredfamily |  -.2886569   .1238691    -2.33   0.020    -.5314358    -.045878
 resp_migrant10 |  -.3175279   .1786185    -1.78   0.075    -.6676137    .0325579
         native |  -.0521672   .1509638    -0.35   0.730    -.3480508    .2437164
   resp_maj_eth |  -.1346802   .1535016    -0.88   0.380    -.4355377    .1661773
          _cons |  -2.002945   .3801035    -5.27   0.000    -2.747934   -1.257956
---------------------------------------------------------------------------------
Note: 0 failures and 1 success completely determined.

. est sto Malawi2

. 
. coefplot Malawi Malawi2, drop(_cons) xline(0) omitted baselevels msymbol(d)  ///
> levels(95)  xtitle({bf:Predicting Having a Title}, size(small)) ///
> legend(off) xscale(range(-1.5 1.5)) xlabel(-1.5(.5)1.5) ///
> headings(single_male = "{bf:HH Gender/Marriage Status}" age = "{bf:}" resp_migra
> nt10 = "{bf:}" ///
> aglanduse = "{bf:}" resp_highinc = "{bf:Income}"  primary_schooling = "{bf:Educa
> tion}"  ///
> land_size = "{bf:}" native="{bf:}" Acquiredfamily = "{bf:}"  resp_maj_eth = "{bf
> :}" contributed= "{bf:}" local_obligation_to_help= "{bf:}", ///
> labsize(2.5)) graphregion(color(white)) grid(between glcolor(white)) ///
> legend(label(1 "Success Index") row(1)  size(small)) ylab(, labs(2.5)) xlab(, la
> bs(2.5)) ysize(6.5)

. 
. *Zambia D1(b)
. logit have_gov_title single_male single_female   resp_highinc resp_lowinc primar
> y_schooling  ///
>  secondaryplus age  land_size aglanduse Acquiredfamily resp_migrant10 ///
>  native resp_maj_eth contributed local_obligation_to_help  if zam_border == 1 , 
> cluster(sqkm)

Iteration 0:   log pseudolikelihood = -827.54275  
Iteration 1:   log pseudolikelihood = -757.02024  
Iteration 2:   log pseudolikelihood = -696.52888  
Iteration 3:   log pseudolikelihood = -695.74447  
Iteration 4:   log pseudolikelihood = -695.74369  
Iteration 5:   log pseudolikelihood = -695.74369  

Logistic regression                             Number of obs     =      3,241
                                                Wald chi2(15)     =     251.48
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -695.74369               Pseudo R2         =     0.1593

                                    (Std. Err. adjusted for 405 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
 have_gov_title |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
    single_male |   .3686902   .2409219     1.53   0.126     -.103508    .8408884
  single_female |   .2952509   .1914467     1.54   0.123    -.0799777    .6704796
   resp_highinc |   .8828029   .1850013     4.77   0.000      .520207    1.245399
    resp_lowinc |  -.1348465   .1830994    -0.74   0.461    -.4937147    .2240217
primary_schoo~g |  -.4425592   .2519145    -1.76   0.079    -.9363025    .0511841
  secondaryplus |   .2675897   .2838425     0.94   0.346    -.2887314    .8239108
            age |   .0054452   .0045365     1.20   0.230    -.0034462    .0143366
      land_size |   .0007118   .0016521     0.43   0.667    -.0025262    .0039498
      aglanduse |   -1.22245   .2226709    -5.49   0.000    -1.658877   -.7860234
 Acquiredfamily |   .1942183   .1784826     1.09   0.277    -.1556012    .5440378
 resp_migrant10 |   .2223001   .1449599     1.53   0.125    -.0618162    .5064164
         native |  -.3681217   .2150591    -1.71   0.087    -.7896299    .0533865
   resp_maj_eth |  -1.053948   .2453702    -4.30   0.000    -1.534865   -.5730316
    contributed |  -.0280475   .1887069    -0.15   0.882    -.3979062    .3418111
local_obligat~p |  -.0742465   .1653223    -0.45   0.653    -.3982722    .2497792
          _cons |  -1.542567   .4126134    -3.74   0.000    -2.351275   -.7338599
---------------------------------------------------------------------------------

. est sto Zambia

. 
. logit have_gov_title single_male single_female   resp_highinc resp_lowinc primar
> y_schooling  ///
>  secondaryplus age  land_size aglanduse Acquiredfamily resp_migrant10 ///
>  native resp_maj_eth if zam_border == 1 , cluster(sqkm)

Iteration 0:   log pseudolikelihood = -863.62529  
Iteration 1:   log pseudolikelihood = -789.14315  
Iteration 2:   log pseudolikelihood = -729.41794  
Iteration 3:   log pseudolikelihood = -728.71642  
Iteration 4:   log pseudolikelihood = -728.71584  
Iteration 5:   log pseudolikelihood = -728.71584  

Logistic regression                             Number of obs     =      3,347
                                                Wald chi2(13)     =     236.01
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -728.71584               Pseudo R2         =     0.1562

                                    (Std. Err. adjusted for 409 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
 have_gov_title |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
    single_male |   .2964736   .2438612     1.22   0.224    -.1814854    .7744327
  single_female |   .3149177   .1850306     1.70   0.089    -.0477356    .6775709
   resp_highinc |   .8435836   .1905688     4.43   0.000     .4700755    1.217092
    resp_lowinc |  -.1178662   .1777539    -0.66   0.507    -.4662575    .2305251
primary_schoo~g |  -.3790851   .2471952    -1.53   0.125    -.8635787    .1054085
  secondaryplus |   .3518553   .2768734     1.27   0.204    -.1908065    .8945172
            age |   .0049779   .0044568     1.12   0.264    -.0037574    .0137131
      land_size |   .0004547   .0015411     0.30   0.768    -.0025658    .0034751
      aglanduse |  -1.190232   .2270201    -5.24   0.000    -1.635183   -.7452811
 Acquiredfamily |   .2004581   .1807463     1.11   0.267    -.1537981    .5547144
 resp_migrant10 |   .2073796   .1464091     1.42   0.157    -.0795769     .494336
         native |  -.3340419   .2172326    -1.54   0.124    -.7598099    .0917262
   resp_maj_eth |  -1.099676   .2394599    -4.59   0.000    -1.569009   -.6303429
          _cons |  -1.595572   .4243679    -3.76   0.000    -2.427318   -.7638264
---------------------------------------------------------------------------------

. est sto Zambia2

. 
. coefplot Zambia Zambia2, drop(_cons) xline(0) omitted baselevels msymbol(d)  ///
> levels(95)  xtitle({bf:Predicting Having a Title}, size(small)) ///
> legend(off) xscale(range(-1.5 1.5)) xlabel(-1.5(.5)1.5) ///
> headings(single_male = "{bf:HH Gender/Marriage Status}" age = "{bf:}" resp_migra
> nt10 = "{bf:}" ///
> aglanduse = "{bf:}" resp_highinc = "{bf:Income}"  primary_schooling = "{bf:Educa
> tion}"  ///
> land_size = "{bf:}" native="{bf:}" Acquiredfamily = "{bf:}"  resp_maj_eth = {bf:
> } contributed= "{bf:}" local_obligation_to_help= "{bf:}", ///
> labsize(2.5)) graphregion(color(white)) grid(between glcolor(white)) ///
> legend(label(1 "Success Index") row(1)  size(small)) ylab(, labs(2.5)) xlab(, la
> bs(2.5)) ysize(6.5)

. 
. 
. *** Appendix E ***
. 
. *Table E1
. *pooled, individ.
. logit trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_
> male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -4995.3841  
Iteration 1:   log pseudolikelihood = -4891.8852  
Iteration 2:   log pseudolikelihood =   -4891.81  
Iteration 3:   log pseudolikelihood =   -4891.81  

Logistic regression                             Number of obs     =      7,242
                                                Wald chi2(8)      =     171.11
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =   -4891.81               Pseudo R2         =     0.0207

                                   (Std. Err. adjusted for 768 clusters in sqkm)
--------------------------------------------------------------------------------
               |               Robust
trust_pay_back |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     exp_title |  -.1749172   .0487455    -3.59   0.000    -.2704566   -.0793778
   exp_highinc |   .0618978   .0560145     1.11   0.269    -.0478887    .1716843
    exp_lowinc |  -.2256321   .0640721    -3.52   0.000    -.3512111   -.1000531
   exp_migrant |  -.4167044   .0488076    -8.54   0.000    -.5123656   -.3210432
     exp_age50 |  -.0595668   .0497809    -1.20   0.231    -.1571356    .0380021
      exp_male |  -.0895723   .0474597    -1.89   0.059    -.1825915    .0034469
 exp_co_ethnic |   .2197955   .0471919     4.66   0.000      .127301      .31229
    mal_border |  -.3992055   .0568698    -7.02   0.000    -.5106683   -.2877427
         _cons |   .6830284    .073831     9.25   0.000     .5383223    .8277345
--------------------------------------------------------------------------------

. *pooled, comm.
. logit help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_ag
> e50 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Iteration 0:   log pseudolikelihood =  -3946.543  
Iteration 1:   log pseudolikelihood = -3913.6342  
Iteration 2:   log pseudolikelihood = -3913.5329  
Iteration 3:   log pseudolikelihood = -3913.5329  

Logistic regression                             Number of obs     =      6,975
                                                Wald chi2(8)      =      61.92
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -3913.5329               Pseudo R2         =     0.0084

                                    (Std. Err. adjusted for 767 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
help_collect_~s |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      exp_title |  -.1730316   .0547829    -3.16   0.002    -.2804041   -.0656591
    exp_highinc |  -.0769848   .0673111    -1.14   0.253     -.208912    .0549425
     exp_lowinc |  -.2349724   .0657872    -3.57   0.000    -.3639129   -.1060318
    exp_migrant |  -.2513174   .0578265    -4.35   0.000    -.3646554   -.1379795
      exp_age50 |  -.0818551   .0574313    -1.43   0.154    -.1944184    .0307083
       exp_male |   -.064667   .0572141    -1.13   0.258    -.1768045    .0474706
  exp_co_ethnic |   .1242081   .0532776     2.33   0.020     .0197859    .2286302
     mal_border |  -.2186999   .0635739    -3.44   0.001    -.3433024   -.0940974
          _cons |   1.529316   .0856187    17.86   0.000     1.361506    1.697126
---------------------------------------------------------------------------------

. *malawi, individ.
. logit trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_
> male ///
> exp_co_ethnic if mal_border == 1 & have_gov_title == 0, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -2515.1268  
Iteration 1:   log pseudolikelihood = -2476.7953  
Iteration 2:   log pseudolikelihood =  -2476.784  
Iteration 3:   log pseudolikelihood =  -2476.784  

Logistic regression                             Number of obs     =      3,629
                                                Wald chi2(7)      =      80.63
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -2476.784               Pseudo R2         =     0.0152

                                   (Std. Err. adjusted for 352 clusters in sqkm)
--------------------------------------------------------------------------------
               |               Robust
trust_pay_back |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     exp_title |  -.1743866   .0699844    -2.49   0.013    -.3115534   -.0372197
   exp_highinc |   .0684701   .0739741     0.93   0.355    -.0765164    .2134566
    exp_lowinc |  -.1460983   .0879807    -1.66   0.097    -.3185374    .0263407
   exp_migrant |  -.3833494   .0678732    -5.65   0.000    -.5163784   -.2503203
     exp_age50 |  -.1160557   .0724672    -1.60   0.109    -.2580888    .0259773
      exp_male |   -.135639   .0679779    -2.00   0.046    -.2688733   -.0024048
 exp_co_ethnic |   .3357993   .0647127     5.19   0.000     .2089647     .462634
         _cons |   .2315933   .0985455     2.35   0.019     .0384478    .4247388
--------------------------------------------------------------------------------

. *malawi, comm.
. logit help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_ag
> e50 exp_male ///
> exp_co_ethnic if mal_border == 1 & have_gov_title == 0, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -2061.9161  
Iteration 1:   log pseudolikelihood = -2051.8946  
Iteration 2:   log pseudolikelihood = -2051.8798  
Iteration 3:   log pseudolikelihood = -2051.8798  

Logistic regression                             Number of obs     =      3,518
                                                Wald chi2(7)      =      20.94
                                                Prob > chi2       =     0.0039
Log pseudolikelihood = -2051.8798               Pseudo R2         =     0.0049

                                    (Std. Err. adjusted for 352 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
help_collect_~s |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      exp_title |  -.1843844   .0734944    -2.51   0.012    -.3284307   -.0403381
    exp_highinc |  -.1016003   .0919879    -1.10   0.269    -.2818933    .0786926
     exp_lowinc |  -.0898989   .0892414    -1.01   0.314    -.2648088    .0850109
    exp_migrant |  -.1892049   .0747209    -2.53   0.011    -.3356553   -.0427545
      exp_age50 |  -.1242457    .078424    -1.58   0.113    -.2779539    .0294626
       exp_male |  -.0556644   .0798292    -0.70   0.486    -.2121267     .100798
  exp_co_ethnic |    .143723   .0729785     1.97   0.049     .0006879    .2867582
          _cons |   1.251032   .1103545    11.34   0.000     1.034742    1.467323
---------------------------------------------------------------------------------

. *zambia, individ.
. logit trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_
> male ///
> exp_co_ethnic if mal_border == 0 & have_gov_title == 0, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -2447.1772  
Iteration 1:   log pseudolikelihood = -2409.2965  
Iteration 2:   log pseudolikelihood = -2409.2648  
Iteration 3:   log pseudolikelihood = -2409.2648  

Logistic regression                             Number of obs     =      3,613
                                                Wald chi2(7)      =      66.00
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -2409.2648               Pseudo R2         =     0.0155

                                   (Std. Err. adjusted for 416 clusters in sqkm)
--------------------------------------------------------------------------------
               |               Robust
trust_pay_back |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     exp_title |  -.1782901   .0681174    -2.62   0.009    -.3117978   -.0447824
   exp_highinc |   .0481483   .0858705     0.56   0.575    -.1201548    .2164514
    exp_lowinc |  -.3098778   .0928272    -3.34   0.001    -.4918158   -.1279398
   exp_migrant |  -.4552015   .0699142    -6.51   0.000    -.5922308   -.3181722
     exp_age50 |  -.0012786   .0680319    -0.02   0.985    -.1346186    .1320615
      exp_male |  -.0445472   .0663264    -0.67   0.502    -.1745445    .0854501
 exp_co_ethnic |   .1008325   .0676804     1.49   0.136    -.0318187    .2334837
         _cons |   .7468004   .0935374     7.98   0.000     .5634705    .9301303
--------------------------------------------------------------------------------

. *zambia, comm.
. logit help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_ag
> e50 exp_male ///
> exp_co_ethnic if mal_border == 0 & have_gov_title == 0, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -1877.3403  
Iteration 1:   log pseudolikelihood = -1856.5732  
Iteration 2:   log pseudolikelihood = -1856.4683  
Iteration 3:   log pseudolikelihood = -1856.4683  

Logistic regression                             Number of obs     =      3,457
                                                Wald chi2(7)      =      39.35
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1856.4683               Pseudo R2         =     0.0111

                                    (Std. Err. adjusted for 415 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
help_collect_~s |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      exp_title |   -.166274   .0820698    -2.03   0.043    -.3271278   -.0054201
    exp_highinc |  -.0554643   .0989652    -0.56   0.575    -.2494326     .138504
     exp_lowinc |  -.3957526   .0959902    -4.12   0.000    -.5838899   -.2076153
    exp_migrant |  -.3218321    .090353    -3.56   0.000    -.4989208   -.1447434
      exp_age50 |  -.0317974   .0846508    -0.38   0.707    -.1977099    .1341152
       exp_male |  -.0746576   .0823121    -0.91   0.364    -.2359863    .0866712
  exp_co_ethnic |   .1030276   .0778629     1.32   0.186    -.0495808     .255636
          _cons |   1.605542   .1143645    14.04   0.000     1.381392    1.829692
---------------------------------------------------------------------------------

. 
. 
. *Table E2
. *Vertical; Follow Headman/Woman Mechanism
. logit neigh_follow_hmw exp_title exp_highinc exp_lowinc exp_migrant exp_age50 ex
> p_male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Iteration 0:   log pseudolikelihood =  -3306.979  
Iteration 1:   log pseudolikelihood =  -3253.654  
Iteration 2:   log pseudolikelihood = -3253.0774  
Iteration 3:   log pseudolikelihood = -3253.0773  

Logistic regression                             Number of obs     =      7,052
                                                Wald chi2(8)      =     108.88
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -3253.0773               Pseudo R2         =     0.0163

                                    (Std. Err. adjusted for 762 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
neigh_follow_~w |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      exp_title |   -.557981    .062861    -8.88   0.000    -.6811864   -.4347756
    exp_highinc |  -.1953373   .0768319    -2.54   0.011     -.345925   -.0447496
     exp_lowinc |    .086039   .0812479     1.06   0.290     -.073204     .245282
    exp_migrant |  -.1753658   .0599497    -2.93   0.003    -.2928652   -.0578665
      exp_age50 |   -.003599   .0625162    -0.06   0.954    -.1261286    .1189306
       exp_male |  -.1220899   .0641289    -1.90   0.057    -.2477803    .0036005
  exp_co_ethnic |   .0174925   .0618864     0.28   0.777    -.1038027    .1387877
     mal_border |  -.1160587   .0760496    -1.53   0.127    -.2651133    .0329958
          _cons |   2.075813   .0976495    21.26   0.000     1.884423    2.267203
---------------------------------------------------------------------------------

. *Horizontal; Share Well Mechanism
. logit neigh_let_use_well exp_title exp_highinc exp_lowinc exp_migrant exp_age50 
> exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -3219.9986  
Iteration 1:   log pseudolikelihood = -3198.6171  
Iteration 2:   log pseudolikelihood = -3198.5202  
Iteration 3:   log pseudolikelihood = -3198.5202  

Logistic regression                             Number of obs     =      6,945
                                                Wald chi2(8)      =      39.84
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -3198.5202               Pseudo R2         =     0.0067

                                    (Std. Err. adjusted for 764 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
neigh_let_use~l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      exp_title |  -.2733306   .0635168    -4.30   0.000    -.3978213   -.1488399
    exp_highinc |    -.15886    .077586    -2.05   0.041    -.3109258   -.0067942
     exp_lowinc |  -.0418129   .0789691    -0.53   0.596    -.1965896    .1129637
    exp_migrant |  -.2311574   .0680728    -3.40   0.001    -.3645777   -.0977371
      exp_age50 |   .0211274   .0645404     0.33   0.743    -.1053695    .1476242
       exp_male |  -.0768694   .0644192    -1.19   0.233    -.2031286    .0493899
  exp_co_ethnic |  -.0125128   .0593375    -0.21   0.833    -.1288122    .1037866
     mal_border |  -.1358189   .0749541    -1.81   0.070    -.2827263    .0110884
          _cons |   1.986219    .099696    19.92   0.000     1.790819     2.18162
---------------------------------------------------------------------------------

. *Diffuse; Observe Way of Life Mechanism
. logit neigh_obs_way_life exp_title exp_highinc exp_lowinc exp_migrant exp_age50 
> exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 0, cluster(sqkm)

Iteration 0:   log pseudolikelihood = -4301.9381  
Iteration 1:   log pseudolikelihood = -4211.8444  
Iteration 2:   log pseudolikelihood = -4211.5172  
Iteration 3:   log pseudolikelihood = -4211.5172  

Logistic regression                             Number of obs     =      6,788
                                                Wald chi2(8)      =     113.57
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -4211.5172               Pseudo R2         =     0.0210

                                    (Std. Err. adjusted for 762 clusters in sqkm)
---------------------------------------------------------------------------------
                |               Robust
neigh_obs_way~e |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      exp_title |   -.135645   .0567497    -2.39   0.017    -.2468724   -.0244176
    exp_highinc |  -.0914815   .0637285    -1.44   0.151    -.2163871    .0334241
     exp_lowinc |  -.0793377   .0659053    -1.20   0.229    -.2085098    .0498344
    exp_migrant |  -.1516173   .0555508    -2.73   0.006    -.2604949   -.0427397
      exp_age50 |  -.0627993   .0524412    -1.20   0.231    -.1655821    .0399835
       exp_male |    .025513   .0537721     0.47   0.635    -.0798785    .1309044
  exp_co_ethnic |   .1251436   .0535375     2.34   0.019     .0202121    .2300751
     mal_border |   .6475374   .0712589     9.09   0.000     .5078726    .7872022
          _cons |   .5614432   .0878596     6.39   0.000     .3892416    .7336448
---------------------------------------------------------------------------------

. 
. 
. *Table E3
. *pooled, only WITH title, individ.
. reg trust_pay_back exp_title exp_highinc exp_lowinc exp_migrant exp_age50 exp_ma
> le ///
> exp_co_ethnic mal_border if have_gov_title == 1, cluster(sqkm)

Linear regression                               Number of obs     =        718
                                                F(8, 261)         =       2.63
                                                Prob > F          =     0.0086
                                                R-squared         =     0.0252
                                                Root MSE          =     .49481

                                  (Std. Err. adjusted for 262 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
trust_pay_b~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |    .017594   .0381711     0.46   0.645    -.0575686    .0927566
  exp_highinc |  -.0613251   .0443698    -1.38   0.168    -.1486934    .0260432
   exp_lowinc |  -.0528214   .0450322    -1.17   0.242    -.1414941    .0358514
  exp_migrant |  -.1082902   .0369012    -2.93   0.004    -.1809522   -.0356283
    exp_age50 |   .0241469    .036532     0.66   0.509     -.047788    .0960819
     exp_male |  -.0559863   .0332221    -1.69   0.093    -.1214037    .0094311
exp_co_ethnic |   .0370412    .039453     0.94   0.349    -.0406455    .1147278
   mal_border |   -.073042   .0417277    -1.75   0.081    -.1552078    .0091237
        _cons |   .6747558   .0573624    11.76   0.000     .5618038    .7877079
-------------------------------------------------------------------------------

. *pooled, only WITH title comm.
. reg help_collect_donations exp_title exp_highinc exp_lowinc exp_migrant exp_age5
> 0 exp_male ///
> exp_co_ethnic mal_border if have_gov_title == 1, cluster(sqkm)

Linear regression                               Number of obs     =        680
                                                F(8, 258)         =       1.09
                                                Prob > F          =     0.3679
                                                R-squared         =     0.0137
                                                Root MSE          =     .42409

                                  (Std. Err. adjusted for 259 clusters in sqkm)
-------------------------------------------------------------------------------
              |               Robust
help_collec~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
    exp_title |   .0180064   .0320655     0.56   0.575     -.045137    .0811498
  exp_highinc |  -.0513398   .0401478    -1.28   0.202    -.1303988    .0277193
   exp_lowinc |  -.0327493   .0351287    -0.93   0.352    -.1019248    .0364263
  exp_migrant |  -.0186296   .0314653    -0.59   0.554     -.080591    .0433319
    exp_age50 |    .026446   .0322083     0.82   0.412    -.0369786    .0898706
     exp_male |  -.0443979   .0334683    -1.33   0.186    -.1103037    .0215078
exp_co_ethnic |   .0395695   .0318915     1.24   0.216    -.0232312    .1023702
   mal_border |  -.0554743   .0367373    -1.51   0.132    -.1278175    .0168689
        _cons |   .8179491   .0478999    17.08   0.000     .7236246    .9122736
-------------------------------------------------------------------------------

. 
. 
. *Table E4
. *this table is replicated by simply subtracting the respective coefficients 
. *(which come from the models reported in Table B3):
. di -.080 - -.039
-.041

. di -.080 - -.029
-.051

. di -.039 - -.029
-.01

. 
. *And then calculating z-scores for these differences using Clogg's method:
. *z = (B1 - B2) / √(seB1^2 + seB2^2)
. *z score threashold is 1.96 for .05 level in two-tailed test
. 
. *hmw v well
. di (-.0803396 - - .0391765) / sqrt((.0092414*.0092414) + (.0091519*.0091519))
-3.1648875

. *-3.165
. 
. *hmw v obs
. di (-.0803396 - - .0291899) / sqrt((.0092414*.0092414) + (.0122279*.0122279))
-3.337171

. *-3.337
. 
. * well v obs
. di (-.0391765 - - .0291899) / sqrt((.0091519*.0091519) + (.0122279*.0122279))
-.65385283

. *-0.654
. 
. 
. log close
      name:  <unnamed>
       log:  /Users/uctqa20/Dropbox/Land&UrbanizationProject/Analysis/Replication 
> Material/replication_jop.log
  log type:  text
 closed on:  16 Dec 2021, 13:00:51
----------------------------------------------------------------------------------
